The cost and risk associated with biopharmaceutical R&D is enormous and the rewards are increasingly elusive. For many young firms, the “valley of death” is a very real obstacle. What can startups (and larger companies) do? In the first blog post based on my interview with H3 Biomedicine CEO Markus Warmuth, we discussed H3’s strategy to propel innovative therapies into the market using a tighter time frame based upon a new research and clinical paradigm that is almost the reverse of traditional biopharmaceutical development. In this segment, Warmuth shares with us some specifics about H3 Biomedicine’s unique approach to drug development.
Q: What is the current approach to drug discovery at H3?
Warmuth: It is very target centric. Our discovery and development is focused heavily on target identification and validation. When systematic gene expression studies started to come out, it came with a lot of hope but then failed the industry. We found out that a change in the expression of a gene doesn’t necessarily mean a change or impact on the course of a disease. Instead, gene mutations have proven very powerful in predicting disease relevance. And, through public efforts like The Cancer Genome Atlas (TCGA) now we have more information at hand to draw conclusions quickly about the specific consequences of mutations. In some cases we find that alternative splicing can provide some insights, and this is an area we are very focused on. However, we have observed that mutations – and those are the majority – can inactivate or change the function of a gene or its product such that the mutated gene might no longer be a viable drug target. Under such circumstances we typically need to find targets that lie downstream or are synthetic lethal to the target – an area we are exploring very heavily.
Q: What is the best method for target validation in drug discovery?
Warmuth: There’s no simple answer. You have to go gene by gene and ask, “What are the key scientific questions that would support the nomination of a given gene or its product as the target for a discovery program?” The right method or technology is the one that gets you to the finish line with the most convincing data. Today, you can’t bank on one specific technology. We’re trying to diversify our methods to find the right answers related to different kinds of targets.
Q: How are you different from “big pharma”?
Warmuth: I’m not claiming that our approach is better, but it’s unique—it’s completely focused on our strategy. In a traditional development paradigm, there are a lot of clinical failures, especially in Phase 2, because the discovery process is very compound-centric. The only time most researchers in drug discovery think about the target is at the beginning of a project, prior to a screen. From then the process and milestones are very compound (or NCE) centric. They do a screen to find hits, they take the “hit” to a lead, keep optimizing the lead candidates, select, and continue to optimize the compound until they’ve arrived at a compound ready to put in the clinic. In this process, many times scientists forget to ask whether the original hypothesis around the target still holds up, because they are rewarded for bringing a compound forward. So it’s all compound-centric. We’re trying to support a target-centric approach. We challenge the target and our hypothesis at every step of the preclinical process, with different methods and compounds. Because of that, we will have by the time we enter the clinic so much confidence in the target that, we’re not trying to just “see what we get.”
Q: What is a druggable target?
Warmuth: That is a target that is amenable, and responsive to current drug discovery approaches. Some targets are considered to be “undruggable,” if their activity cannot be modulated using compounds that are found in typical libraries of drug – like molecules. But with the right library, you might be able to “drug” the target. There are two important considerations here:
- Chemical space; some compound libraries are geared to one class of target, but sometimes one still needs to switch to another class of target. Research consortia would be great; they would enable us to exchange our chemical libraries more easily, so we wouldn’t have to reinvent our own chemicals. At H3 we’re building a proprietary chemical library that’s suitable for certain classes of target, including those which have traditionally been considered to be undruggable.
- Have the right assay in place. Many organizations settle too quickly on an established assay. We’re trying to establish a screening platform that’s medium throughput (3,000-5,000 compounds at a time). We can then look at different versions of an assay to find the right one. We need to have the right components in an assay and make sure the assay allows the enzyme to be regulated the way it would be in a cell.
Q: Are you developing companion diagnostics? How do they fit within your drug development strategy?
Warmuth: This is absolutely key to our strategy, which is patient-centric. Every project we’re working on starts with a hypothesis derived from real patients (or, more specifically, patient samples). Then, we always want to explore the effects of a drug candidate in a population related to the population we started from with our original genomic data (we would not study the same patients, but a similar population with same mutation). But we need to identify them with companion diagnostics. This area will get more complicated as patient populations get smaller and smaller. Today, target-centric developers are looking at something that happens in 5 to 10 percent of a certain cancer type, which makes recruiting for clinical trials challenging. As an example, our lead project is on a mutation found in a gene called SF3B1. We know this mutation occurs in multiple different cancer types, but in some it is very rare, in the order of 3 to 5 percent. It will be crucial to have the right companion diagnostic in place to allow selection of those patients once we are in a clinical trial.
Q: Where do you believe H3 will be in five years?
Warmuth: We want to be at the stage where we’re very close to clinical proof of concept. We’ve validated a new discovery paradigm that is focused on human biology, the human cancer genome and new metrics of drug discovery. It would be nice at that time to be able to reflect on the results of the strategy that we have discussed today; to have access to more models, to more closely incorporate patients as part of the discovery process, and to be able to access patients more easily for hypothesis testing.
What do you think of H3’s model? How have advances in genomics affected your product development strategies? Is there a way to digitize this new biological knowledge, or even create tools to help uncover other biological trends? Please share your thoughts with us here.
Tags: biological trends, biomedicine, biopharmaceutical, drug development, drug therapies, genomics, H3 Biomedicine
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As many of you know, R&D progress in drug discovery has been a challenge for “big pharma” and biotech firms alike. The costs and risks are enormous, and the rewards are increasingly elusive. For many young firms, the “valley of death” is a very real obstacle as cash burn rates exceed the pace of successful delivery of new drugs to market. What can startups (and larger companies) do? Markus Warmuth, CEO of Cambridge, Massachusetts-based H3 Biomedicine, talked with us about his company’s strategies for giving drug discovery a much-needed boost.
H3 Biomedicine Inc. is a privately held, uniquely structured oncology discovery enterprise. H3 is applying the expertise of leading scientists to the integration of insights from cancer genomics with innovative capabilities in synthetic chemistry and tumor biology to discover patient-based, genomics-driven, small molecule drugs, which represent the most promising current opportunity in cancer therapeutics. H3 Biomedicine will achieve its goals through a unique relationship with the pharmaceutical company Eisai Inc. Eisai has pledged up to $200 million in research funding to H3 Biomedicine, as well as additional support for the clinical development of H3 Biomedicine programs.
Q: What does H3’s drug discovery approach say about the current healthcare economic climate?
Warmuth: Our scientific operation and implementation of strategy is starting at a time when there’s a lot of genomic information becoming available from large numbers of cancer patients. There’s The Cancer Genome Atlas (TCGA) and the International Cancer Genome Consortium (ICGC). It’s an absolutely fantastic time for drug discovery using a human biology or genome based approach. Now that we have genomic information available through public portals on 5,000 or more patients, we can be very systematic.
For financing, our company is based on a very different model from most companies within the small-sized biotech sector. Because we are funded by Eisai, we are less focused on short-term success and exit strategies. Human biology is very complicated; you can’t resolve issues in 3-5 years – so we are fortunate to have the ability to take a longer term approach to oncology drug discovery.
Q: How does your model let you innovate?
Warmuth: We are looking at a 6- to 8-year trajectory. This gives us time to dig deeply into genomic information, and to create biological hypotheses. On the discovery end, it prepares us to find the right chemicals to address genetic changes. We can take a detailed look at the chemical space and enzyme structure/function, and marry that with human biology. It’s relatively easy to come up with compounds, but we don’t want to ignore the question of whether a particular kinase is biologically relevant—whether an inhibitor of that kinase is more than just an interesting chemical, and whether it actually has a beneficial effect in vivo.
Q: What does it mean to you as CEO to have the luxury to innovate?
Warmuth: It’s key to have the right tools to test the hypothesis. The advantage of genomics data (and access to genomics databases) – the way it is generated by TCGA and ICGC – is that we have consistent, multilayered data from many different patients. We have information on normal tissue but also disease-related RNA sequencing data, which allows us to measure gene expression but also delivers insights into alternative gene splicing, SNP arrays to get into gene copy number changes, and so on. With this, we can start to do some really cool biological networking exercises. We do, however, need to set up new models—cell lines do not enable the same level of analysis as human samples and related genetic analyses.
Q: Why is cancer the preferred disease state for H3?
Warmuth: It’s an obvious starting point. It’s the disease with the most genomic and mechanistic information available at this point, in part because cancer tissue is more accessible than tissue related to other disease areas (compared to neuroscience, for example). Other disease areas may be catching up; there are large-scale SNP genotype and other genomic studies advancing rapidly for many other human diseases. We’re using that genetic and genomic information to drive multiple disease/discovery hypotheses.
Q: I believe that cancer is on the leading edge of drug discovery. Do you agree?
Warmuth: I certainly agree. In the areas of understanding human disease biology, relating to genetic and epigenetic changes, signaling pathways and changes in tissue, cancer is well ahead. If you think about it, cancer is probably the disease where the diseased tissue changes most, compared to normal tissue.
We’ll share more from our discussions with Dr. Warmuth in a future post on new directions in target validation, the value of open-source genomics data, and how H3’s discovery strategy differs from that of big pharma. In the mean time, you can read about H3’s most recent news announcement here.
What do you think of H3’s model? How have advances in genomics affected your product development strategies? Is there a way to digitize this new biological knowledge, or even create tools to help uncover other biological trends? Please share your thoughts with us here.
Tags: big pharma, biomedicine, cancer therapeutics, drug discovery, H3 Biomedicine, Markus Warmuth, open-source genomics data
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As we’ve discussed, Eric Topol’s “How to Change Medicine” provides valuable insights on how tailoring treatments in the clinic could boost health care effectiveness and lower costs. But we at Popper and Company would like to see industry take an additional step by using the techniques Dr. Topol recommends for patient care to develop drugs more efficiently.
The technology Topol recommends to stratify patients could also streamline drug development. In fact, the FDA is suggesting that faster approvals of antibiotics could result from smaller clinical trials that test antibiotics targeted against drug-resistant bacteria.
We now know enough about molecular biology that we can apply genomics/proteomics/metabolomics to drug development (DNA screening, identifying and validating surrogate markers, or characterizing tumors by genetic makeup). However, tailored treatments can run into challenges, including heterogeneity in tumor cells as recently described in the New England Journal of Medicine, that resist even targeted biological treatments.
But as our pace of understanding accelerates, even knowing that tumor cell heterogeneity exists can allow us to use animal models to find correlations between gene expression and proteomics that show the progression of cancer.
There is currently a tsunami of genetic information that is being applied to diagnostics and disease management. Genetic testing is available for 2,000 conditions, and that number is expected to rise rapidly. Better preclinical characterization of drugs, more targeted applications of new and existing compounds, and a focus on actionable genes like EGFR, BRAF and many others could make tailored, cost-effective treatments a reality.
Do you think the life sciences industry has enough biological knowledge at our disposal to create a new world of tailored treatments? Or does the tumor heterogeneity study show that what we don’t know can stymie our efforts? What other methods can tailor treatment development? Share your ideas and thoughts with us here.
Tags: Dr. Eric Topol, Dr. Topol, drug development, health care effectiveness, How to Change Medicine, tailored treatments, treatment development
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There appears to be a full-scale race underway to bring affordable next-gen DNA sequencing into diagnostics and clinical medicine, as demonstrated over the past week or so by Roche’s hostile $5.7 billion-dollar bid for Illumina. Roche’s move should come as no shock. The multi-national healthcare giant has been a leader in diagnostics by virtue of several earlier acquisitions. But this action signifies a formal acknowledgment that next-gen sequencing will be part of the diagnostic and clinical toolbox—perhaps sooner than we thought.
Roche’s aggressive move may be motivated by an optimism that arose from whole genome-sequencing on the individual level. We noted this rising tide of optimism early last year, and many advances have been reported since then. We’re certainly seeing many examples of the application of next-gen sequencing to diagnose disease and to help match the right drugs to the right patients. Examples of companies that are staking claims over the early application of targeted sequencing of specific panels of genes for diagnostics include our client Multiplicom, which develops CE-marked, multiplexed PCR kits to generate templates for next-gen sequencing, and Foundation Medicine, which is using targeted sequencing to help diagnose certain cancers and to guide the treatment of cancer patients.
But when viewing the widespread adoption of DNA sequencing, there’s a vast difference between the targeted sequencing of selected panels of genes, exome sequencing, and whole genome sequencing. As noted in our coverage of the 2011 Molecular Med Tri-Con, the challenge of interpreting the large reams of data gathered from scanning the full genome remains the elephant in the room. We will be attending this year’s Tri-Con later this month and look forward to hearing about progress in this regard.
In addition to the challenge of data analysis, we still face some persistent technical issues. Although the cost of DNA sequencing is declining rapidly, there are significant improvements to the technology that are yet to be realized. Speed, cost, and accuracy will be key drivers of clinical and diagnostic DNA sequencing. Although costs are declining rapidly, most next-gen sequencing platforms are somewhat error prone and they have relatively slow cycle times. The accuracy problem is overcome by re-sequencing samples to a high level of coverage or redundancy to help eliminate stochastic errors. High sequence coverage is also needed to identify cancer causing mutations that are present at low frequency and in samples that often contain a mixture of both healthy and cancerous cells.
In clinical applications where patients’ lives are at stake, an accurate diagnosis is crucial. So improved accuracy will not only generate more reliable data, but will also reduce the computational burden, and lower the overall cost and the time required for analysis. Achieving the 1000x sequencing coverage that is required for cancer-related applications is a weighty burden. Sequencing platforms with higher levels of accuracy, lower cost and higher speed will provide a significant advantage. LaserGen is a forward-thinking company (and Popper and Co client) that has developed advanced sequencing chemistry with greatly improved accuracy, reduced cycle times, and lower cost relative to existing next-gen chemistries. This combination of improved speed, cost, and accuracy could help to drive more widespread adoption of next-gen sequencing for clinical and diagnostic applications.
Also, as noted in a recent Bloomberg News article about young twins whose illness was clearly identified via genome sequencing, many obstacles, including lack of health insurance coverage for sequencing, will likely impede progress. Until payers wrestle with a future that includes a populace that is well-informed about their genetic predispositions, even affordable gene-sequencing may find itself relegated to a pile of non-insurable “preventative” claims. And as mentioned earlier—we still do not fully understand the function of every part of the genome.
Ultimately, I believe Roche’s acquisition of Illumina—if it goes through—will be good for the gene sequencing industry, good for Roche’s competitors and good for healthcare consumers. The buyout of Illumina helps validate the idea that sequencing will become part of the diagnostic and clinical toolbox in the near future. Whether companies realize this and plan accordingly, or whether the wave simply carries everyone along, remains to be seen.
Do you agree that this is a good move for the diagnostics and sequencing industry as a whole? What might it mean for your company or your spot in the life science industry? Please share your thoughts with us here.
Tags: diagnostics industry, DNA sequencing, illumina, next-gen DNA sequencing, Roche, sequencing industry, universal DNA sequencing
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Last week, we discussed new drug approvals that reflected a paradigm shift for cancer drug development and for treatment of people living with cancer. Finally, we are starting to see a matchup of specific diagnostics with targeted biotherapeutics to address (effectively, we hope) targeted treatments for smaller groups of cancer patients.
Just recently, the American Society for Clinical Oncology (ASCO) urged the use of new biological knowledge to develop treatments faster, design more targeted clinical trials, and use information technology to integrate once-separate translational and clinical research. ASCO even says that targeted therapies can improve clinical trial responses from 8 to 30 percent. Also, just recently, the FDA gave itself a pat on the back as it highlighted its recent innovative drug approvals, with targeted cancer therapies included among those on the list.
What both of these events underscore is that we’re now finally seeing real co-development of drugs and companion diagnostics. This is a refreshing departure from the old system in which tests were developed after the drug was approved, often to rescue certain drugs that were facing rejection or had been pulled from the market.
We expect this trend to continue and will probably start to see new tests in two categories—
- Tests designed to include or to qualify patients. For example, if the specific drug target is present or activated, say, by a genetic alignment that creates the target (like Bcr-Abl fusion for leukemia), or mutations activate the target (like V600E in BRAF for melanoma), it will be sensitive to inhibitors.
- Tests designed to exclude or to disqualify patients. An example would be tests that determine the vicinity of the target pathway to determine if pre-existing mutations would enable the cancer to bypass drug activity (like KRAS mutations that circumvent EGFR inhibition).
While this may bring optimism to patients and investors alike, Ken Walz, my colleague at Popper and Co., has a warning:
“Don’t discount the economic impact of this new world of cancer therapies. Pharmaceutical companies will at first be hesitant to abandon their blockbuster strategy. Niche drugs would not be sustainable under their cost structure. If discovery and development costs stay the same, the price of niche drugs would have to be very, very high. But if ASCO’s goals come true, R&D costs could realistically come down. And a portfolio of niche drugs could work very well, indeed.”
Good food for thought, considering how many times that light at the end of the cancer therapy tunnel was just a reflection off more muck. What do you think? What does the future of cancer therapy look like? Or the future of pharmaceutical development and R&D investment, for that matter? Share your thoughts with us.
Tags: cancer, cancer drugs, cancer therapies, cancer treatment, companion diagnostics, drug discovery
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In a recent paper that spawned news articles and blogs worldwide, researchers announced the generation of human embryos that could produce stem cells. The announcement marks another step in the use of stem cells to treat a number of disorders, including diabetes, cancer, and neurodegenerative disease. But while this research, conducted by Dieter Egli, Scott Noggle, and their colleagues at the New York Stem Cell Foundation, holds much promise, it also demonstrates the daunting obstacles that block the path to stem cell therapy.
The biggest technical obstacle to stem cell therapy has been generating and isolating enough early-stage stem cells. It is these cells that have the greatest developmental potential because they can most readily differentiate into a mature cell that, in turn, could be used for cell therapy and regenerative medicine.
The discovery that adult cells could be “reprogrammed” into inducible pluripotent stem cells (iPSC) offered promises of stem cell therapy while sidestepping ethical issues arising from embryonic stem cell therapies. However, iPSCs presented technical issues that so far preclude their therapeutic potential. Bone marrow, cord blood, and adipose tissue also produce stem cells, but on an extremely limited basis.
So embryonic stem cells remain the “gold standard” (although iPSCs could still become the predominant technology). In their work, Egli and Noggle produced 13 embryos by transferring nuclei from the skin cells of diabetic patients into human eggs. These eggs surpassed previous attempts at human stem cell production; they could reach the 100-cell blastocyst stage (the earlier attempts never got past 10). What was the difference? The group transferred a diploid nucleus (from the somatic cell) into an oocyte that still had its haploid genome. Now, the cells could differentiate. But they did so with a triploid genome, a result that blocks their suitability for therapeutic use.
Some scientists feel this is an insurmountable obstacle. Others think that too many oocytes are needed to produce one viable stem-cell producing embryo (Egli and Noggin’s work needed 270 eggs to produce 13 embryos).
Clearly, cell therapy is becoming an important part of the pharmaceutical landscape, and the technical hurdles cleared by this research are impressive. But can the remaining issues be resolved? Can we ever get enough oocytes for therapeutic use? Will we ever be able to guide embryonic stem cell differentiation sufficiently to create every one of the approximately 230 cell types that make up the human body? Or will advances in iPSC research surprise us (as they have in the past)? Share your thoughts with us – we’d love to hear them.
Tags: human embryos and stem cells, stem cell research, stem cell therapy, stem cells, stem cells from embryos
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In his latest Wall Street Journal column (“Drugs That Are as Smart as Our Diseases”), biologist/author Matt Ridley bemoans the plummeting efficiency of drug discovery in the pharmaceutical industry. He points to a disturbing paradox: while identifying and sequencing genes of pathogens and cancer cells has become much cheaper in a short period of time, the number of new drug candidates (based at least in part on our knowledge of those genes) has dropped. According to Ridley, new molecule approvals per billions of dollars of inflation-adjusted R&D amounts to no more than one percent of the number of approvals in 1950. And as we’re all aware, this decline in innovation is all the more dire because the pharmaceutical industry needs to replace so-called “blockbuster” drugs that are about to lose their patent protections if it is to continue to keep investors satisfied and fuel future innovation.
So, why hasn’t the same industry that gave us statins, Herceptin®, and vaccines come up with a new generation of treatments? The biggest problem might lie in its success. Researchers today confront an enormous—and growing—amount of genetic and biochemical information as the search continues for newer, more effective drugs. As we generate more data, we are increasing our understanding of the complexity of biological processes underlying disease states. While this better understanding can lead to innovation, it also has uncovered obstacles. For example, scientists have found that signaling pathways leading to cancers are replete with redundancy, shortcuts and other molecular detours that block the activity of cancer drugs. Sometimes, these pathways can help eliminate or prevent cancer; at other times they can exacerbate it.
It also has become increasingly clear that cancers are not what we originally defined them to be. There are 200 types of cancer; most are defined by the tissue in which they are predominantly found or in which they originated (in the case of metastasis). This location-based classification was useful for surgical solutions (which were once the only options available), and could provide some sense of prognosis. However, these location-based classifications do not capture the biological diversity of cancer.
Herceptin® provides a useful glimpse into cancer’s complexity (as well as into the challenges in treating cancer’s manifestations). When introduced, Herceptin proved very effective against breast cancers triggered by mutations in HER2. However, many cancers are resistant to Herceptin, and researchers are now using a “Whack-a-Mole” approach, relying on successive hammer attacks to fight multiple mutations and pathways that pop-up to bypass effects of the “targeted” drug. In fact, Tanizaki and colleagues (full text by subscription) found that HER2, HER3 and MET are factors not only in breast cancer, but lung cancer as well. It’s possible that the “Whack-a-Mole” approach can address the multiple targets that characterize the molecular mechanisms of cancer, while addressing cancers by their genomic and signaling profiles. But that approach will become more effective in those cases where we can simultaneously hit multiple drug targets. It involves taking cancer treatment to the next level.
Clearly, finding the “moles” and utilizing “multiple hammers” will require more and more information, and represents a change in drug discovery strategy from the “one disease-one target” biochemical approach. The World Health Organization has recommended reclassifying lung cancers (subscription required for full text) by immunohistochemical criteria, for example, and continues to work on expanding definitions of cancers to gene or cell signal.
Revisiting the earlier-raised question of “Why the dearth in a new generation of treatments,” I wonder: Will this change in drug discovery strategy help us develop more effective drugs for cancer patients? Can advances in areas like systems biology find more answers that will lead to new options in the treatment of cancer and other diseases? Can we move beyond the “Whack-a-Mole” hammer to make smarter treatments? Please share your thoughts with us here.
Tags: biochemical, cancer, cancer cells, cancer drugs, cancer treatment, drug discovery, gene sequencing
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It’s increasingly clear to anyone who deals with human health – from the bench biologist to the clinical oncologist – that humans are a heterogeneous species. As a result, a drug that works well in one individual may not work at all in another. Thus, the field of targeted (or personalized) medicine came about so doctors could optimize patient care through the use of genetic and biomarker testing. Such tests help identify patients who are (or who are not) most likely to respond to a given therapy. The field is often promoted as a way to get the “right drug to the right patient at the right dose.”
Correct dosing is critical because about 25 percent of all outpatient prescription drugs in the U.S. are taken by patients with genetic variations (specifically, polymorphisms) that affect absorption, metabolism or excretion of those drugs. Again, at risk of stating the obvious, human beings are heterogeneous.
Today, genetic tests are widely used to identify so-called “fast” and “slow” metabolizers of specific drugs and to adjust the dosage according to how long the drug may persist in an individual’s body. Other tests are used to determine whether patients carry drug targets that are sensitive to drugs (like Herceptin), or whether they harbor mutations that will render drugs inactive, either through resistance (from conditions like HIV) or via the presence of alternate pathways that can circumvent—or short circuit—the effects of a drug.
These current tests can therefore reveal whether mutations have either a pharmacokinetic (i.e., drug metabolism) effect or a pharmacodynamic (i.e., drug target) effect. Genetic and biomarker tests are also proving to be important in the management of many diseases, particularly those for which there are multiple therapeutic options and in those cases where the underlying pathology is caused by one of several possible mutations or other risk factors (e.g., breast cancer and diabetes).
While the U.S. Food & Drug Administration website lists approximately 115 biomarker tests associated with just over 100 approved drugs, researchers are moving quickly to add additional tools to the personalized medicine toolbox. One exciting new advance is the use of induced pluripotent stems cells (iPSC) to test drug response directly in cells isolated from individual patients. The idea is that skin cells, or blood cells, can be isolated from individual patients and used to generate iPSC, which are then induced to differentiate into multiple cell types such as neurons, cardiomyocytes, and hepatocytes. Such cells harbor the genetic legacy of the patient from whom they were derived, and therefore a response (or non-response) of those cells to various drugs can be used to predict how a patient will respond.
The biotech firm, Cellular Dynamics International, which was co-founded by stem cell pioneer James Thomson, is now trying to undertake this approach. The firm is currently focused on the creation of iPSC-derived cardiomyocytes. However, it soon plans to add other cell types, including those from liver, brain and blood to the list.
So how “futuristic” is all of this? The good news is that many of the tools needed to realize the vision of personalized medicine are rapidly coming together. The combination of genomics, proteomics, stem cell biology, and other technologies are providing the information that is needed to drive the process. The challenge lies with the drugs themselves—the industry needs to apply the same tools more efficiently to guide the drug discovery process. Research, testing, approval, and application should work in tangent because the faster the next generation of drugs come into play and are FDA approved, the faster these new protocols of personalized medicine can be applied to save lives.
We also need to organize and apply the knowledge we’ve gained in genetic studies over the past ten years and apply this knowledge to evolving research. Effectively communicating the on-going research and applications of personalized medicine to the medical community will mean we’ll see designer drugs that are targeted to ever more specialized groups of patients. That day is on the near horizon, something that is of benefit to all of us, doctor and patient—the emergence of truly personalized medicine.
Are you involved in the discovery and/or development of companion diagnostics and/or drugs designed specifically for particular patient subsets? What do you see as the critical next steps to continue moving towards matching drugs with patients who will benefit most? Please share your thoughts with us here.
Tags: biomarker testing, companion diagnostics, drugs, genetic testing, personalized medicine, Popper and company, targeted medicine
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Though the life sciences industry is making great progress with genetic tests and targeted therapies, a recent article in the New York Times (“How Bright Promise in Cancer Testing Fell Apart”) exposes disturbing and cautionary insights into the application of this technology. The article revealed that research on the application of complex genetic tests to match cancer patients to the best available therapy may not be as promising as it seems.
The article describes a situation that emerged just over a year ago that raised serious doubts about a test developed by researchers at Duke University. The test used gene expression signatures to characterize tumors and to identify those most likely to respond to different drugs.
Duke researchers published several papers that described the discovery and validation of the test, and clinical trials were initiated. A company was set up to provide the test and clinicians at Duke were using the test to guide drug treatment in cancer patients. However, at least one patient died within months after receiving cancer treatment based on what is now known to be a worthless test. Clinical trials were halted and scientific papers in prestigious journals were retracted. Pressure to halt clinical trials came from independent investigators who could not replicate the findings and who found serious deficiencies in the original data on which the test was based.
The Times article highlights several difficult issues. Foremost was the suggestion that data related to genetic research is too complex to be managed properly under standard research conditions. As stated by Dr Lajos Pusztai (researcher at the M.D. Anderson Cancer Center at the University of Texas) in the Times article, it takes a fully diversified staff to effectively manage and analyze the complexity of data in genetic studies. He went on to say, in what was clearly a warning to the community, that unless such a team is in place, researchers “…oversee a machine they don’t understand.”
Genomic technologies hold great promise for the future of targeted therapies and for associated diagnostics. We have seen the successful application of tests based on similar technology and we are starting to see many more tests based on next-generation DNA sequencing, epigenetic testing, SNP analysis, metabolomics, protein expression profiles and other multivariate approaches.
However, based on the incident at Duke (and others including Ovasure and Ovacheck), it seems some researchers (and investors and patients) may still be relying too much on limited validation studies that fail to integrate the complexity of the disease and recognize the level of risk in rapidly evolving scientific strategies. As mainstream media cover these industry setbacks, doubt about the underlying technology itself is planted in the minds of the general public.
So what can those of us within the industry do to address these warnings and quell public concerns?
First, we appreciate that many tests are developed using complex methods that can be difficult to apply. The data is so complex that only a computer can do the appropriate analysis – and the clinical utility is only as good as the underlying quality of the data (which relies on patient selection as well as assay performance and clinical correlation) and associated analysis methods. We need to stress these underlying principles in our dialogue with all stakeholders.
Second, we must remain vigilant about the importance of both clinical validation and analytical validation prior to the adoption of such tests in any setting. Many research groups can become enamored of preliminary data, but hard slogging is needed to ensure that a test is reliable and reproducible and that it can be used to address clinically important questions.
How can we enable better or more streamlined validation? Do you think more regulatory oversight is needed in this area? What is the role of consortia and biobanks? What might be the impact of the issues on the entry of sequencing-based diagnostics into clinical use? We welcome your thoughts on this important topic.
Tags: cancer, cancer research, clinical trials, Duke, genetics, scientific vulnerability
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For the thousands of life science industry representatives who attend the Biotechnology Industry Organization Annual Convention each year, the last few days have been similar to a favorite holiday: exciting, long-awaited, and too quickly over. As we wrap up our #BIO2011 experience and say good-bye to Washington, DC, I took a few moments to look back on many inspiring networking and learning opportunities only available at this kind of international event.
Within the span of a few days, my colleague Caroline Popper and I have been able to meet with clients, colleagues and new contacts from states such as Michigan, California, Texas, and Massachusetts. Furthermore, a large exhibit hall showcasing different regions of the world’s biotech industry has been an extraordinary venue to network with international visitors. I traveled to DC from my office in Toronto, and was able to meet with life science representatives from Australia, New Zealand, Spain, and Italy, sharing with them ideas and guidance for how to establish a presence in North America.
Aside from the terrific networking opportunities, the sessions offered during BIO proved to be illuminating and informative. On Tuesday, for example, there was a much-anticipated panel featuring M2Gen and Banyan Biomarkers, companies hailing from Caroline’s home base of Florida. This popular session was one of many focusing on personalized medicine – specifically the potential to provide increasingly effective diagnostics and treatments tailored to individual patient groups.
Also on Tuesday, the Governor of Maryland hosted a special #BioMaryland panel discussion in the state’s exhibit hall pavilion that featured pioneering life science leaders from organizations like MedImmune and the Institute for Genome Sciences.
In addition, the U.S. healthcare system was the topic of several talks during the course of the conference. Many presenters believe that the demand caused by the aging population will render cost cuts insufficient to save the system. Thus, some emphasized the importance of innovation to reduce the per capita cost of healthcare. And while we didn’t resolve the problems of the healthcare system over four short days, the buzz around future innovations provided some optimism.
Following are some additional takeaways from the convention as recounted by a few fellow attendees across the life science industry. Please share your own point of view in the Comments section below.
On exhibiting/exhibit hall at #BIO2011:
“From an exhibitor perspective, fewer people ‘trick-or-treating’ and more seeking us out to talk partnering. Good trend, in my opinion!” – April Finnen (Twitter: @dynport), Communications, DynPort Vaccine Company LLC (DVC)
“The highlight for me was the Maryland showcase featuring devices, drugs and diagnostics ‘made in Maryland.’ I also took a lot away from the #BioMaryland panel led by Governor O’Malley in the Maryland Pavilion, which featured prominent women researchers and executives.” – Andrea Vernot (Twitter: @AndreaVernot), Assistant Secretary, Marketing & Communications, Maryland Department of Business and Economic Development
On partnering opportunities and meetings at #BIO2011:
“My main purpose in going to BIO was to look for potential research collaborators in nanobiotechnology. Pretty much everyone I met there was someone I would not have ever interacted with otherwise. For example, I talked to people from the European Union who had information about a collaborative research program and routes to funding that I knew nothing about. All the information I gathered and contacts I made possess the opportunity for potential collaborations.” – Mary Spiro (Twitter: @Mary_Spiro), Science Writer, Johns Hopkins Institute for Nanobiotechnology and Baltimore Science News Examiner
“The conference placed a higher premium on the one-on-one interactions versus prior years. The partnering system has really made it easy to meet with other companies.” – Brian Rosen, Vice President, External & Government Affairs, VBI Vaccines
“The international participation was a significant benefit in making connections that would either be impossible or a significant challenge. I made a connection with a Chinese representative that I am confident will bring value.” – John J. Trizzino, Senior Vice President, Business Development, Novavax, Inc.
On the role of social media at #BIO2011:
“Best of BIO? Definitely the social media connections: Conversations started weeks ago and many people felt like friends coming in.” – Ken Grant (Twitter: @kengrantde), Sales and Marketing Director, Analtech, Inc.
“Unofficial Tweetups at major meetings such as BIO provide a good opportunity to put a name to a face and build personal relationships.” – Pieter Droppert (Twitter: @3NT), Science Writer, Management Consultant, Author of http://biotechstrategyblog.com
Were you at the BIO 2011 Convention? Add your comments below.
Tags: #biomaryland, bio2011, Caroline Popper, life science, Shane Climie
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