25 October 2010

Translational research in cancer makes a big splash in Nature (Part 2)

By |2010-10-25T00:00:00+00:00October 25, 2010|Biomarkers, Cancer, Drug Development, Drug Discovery, Personalized Medicine, Strategy and Consulting, Translational Medicine|


In Part 1 of this series, we began a discussion of a new, disruptive strategy for clinical trials of oncology drugs, which had been outlined in a Perspective by Drs. Johann S. de Bono and Alan Ashworth, and published in the 30 September 2010 issue of Nature.

This strategy, which these authors call the personalized medicine hypothesis-testing strategy, is aimed at testing targeted drugs that have been developed via biology-driven drug discovery. Such a strategy begins with a strong biological hypothesis that a particular altered molecular target is critical for the malignant phenotype of a particular cancer. Based on this hypothesis, drug discovery researchers develop both targeted drugs that are specific for these altered targets, and biomarkers that can be used to determine which patients have tumors that express the target, and thus are most likely to benefit from treatment with the drug.

Following preclinical studies, clinical researchers test the drug in patients whose tumors express the target, aiming for proof of mechanism and proof of concept in early clinical trials. This involves the use of rapid dose escalation and adaptive trial design. Following these early trials, the researchers go on to conduct Phase 3 clinical trials, aiming at registration. This strategy is designed to reduce clinical attrition and the time and cost of clinical trials, and to develop superior, targeted drugs that provide greater patient benefit (in terms of progression-free survival) than the typical new oncology drugs that reach the market.

In the de Bono and Ashworth article, the authors provide several examples of successful hypothesis-testing clinical trials using this strategy. In this blog post, we discuss three of these examples, one of which is a “classic” that should be familiar to most of you, another which we have discussed in previous articles on this blog, and a third example that is based on Drs. de Bono and Ashworth’s own research.

Imatinib (Novartis’ Gleevec/Glivec)

The “classic” example of the use of a personalized medicine hypothesis-testing strategy is the development of imatinib (Novartis’ Gleevec/Glivec).  This drug was originally designed as a specific inhibitor of the ABL tyrosine kinase, which is stuck in the activated conformation in the BCR-ABL fusion protein. BCR-ABL is the “driver” mutation in Philadelphia chromosome-positive chronic myeloid leukemia (CML). Imatinib was also found to be specific for two other tyrosine kinases, c-Kit and the platelet-derived growth factor receptor (PDGFR); these findings have led to the use of imatinib to treat other cancers, especially gastrointestinal stromal tumors (GIST). We discussed the role of Dr. Brian Druker (Oregon Health Sciences University in Portland) and Nicholas B. Lydon (then at Novartis) in the development of imatinib in an earlier blog post.

The 2001 published Phase 1 clinical trial of imatinib in CML led by Drs. Druker and Lydon, and clinician Charles L Sawyers, M.D. (Memorial Sloan-Kettering Cancer Center/Howard Hughes Medical Institute) is what Drs. de Bono and Ashworth called “a landmark paper” in the use of a personalized medicine hypothesis-testing strategy to demonstrate the efficacy and safety of a targeted oncology drug. The development of imatinib for CML was made possible by basic research that showed that the BCR-ABL fusion protein (which is generated as the result of the translocation that produces the Philadelphia (Ph) chromosome, the characteristic genetic abnormality of CML) alone was sufficient to cause CML, and that the tyrosine kinase activity of the ABL moiety of the protein was required for its oncogenic activity. Researchers then discovered a compound, imatinib, that was highly specific for BCR-ABL, c-kit, and PDGFR.

The Phase I clinical trial (which took place in 1999) was a dose-escalation trial of imatinib in 83 patients with chronic-phase CML in whom treatment with interferon-alpha had failed. The primary endpoint of the trial was the safety and tolerability of the drug; efficacy was a secondary endpoint. Imatinib was found to be well-tolerated, and a maximum tolerated dose was not identified in this trial. Complete hematological responses (defined by reductions in the white-cell and platelet counts) were seen in 53 of 54 patients who received 300 mg per day or more of imatinib; these responses typically occurred in the first four weeks after initiating treatment. Cytogenetic responses were defined by the percentage of blood cells in metaphase that were positive for the Ph chromosome, ranging from major responses (zero to 35% of Ph chromosome-positive cells) to minor responses (36-65% positive) to no response (over 65% positive). Of the 54 patients treated with doses of 300 mg or more, 29 had cytogentic responses, including 17 with major responses; seven of these patients had complete cytogenetic remissions (durable zero percent Ph chromosome positive).

Blood samples were taken to determine whether BCR-ABL tyrosine kinase activity had been inhibited by in vivo treatment with imatinib. The researchers observed dose-dependent inhibition of BCR-ABL tyrosine kinase activity. This constituted proof of mechanism of the drug, while the antileukemic activity of imatinib in the trial constituted proof-of-concept.

The researchers then conducted Phase 2 clinical trials, which confirmed and extended the results seen in Phase 1. The FDA approved imatinib in May 2001, less than three years after initiation of clinical trials. This rapid approval was made possible by the FDA granting imatinib a Fast Track designation and Accelerated Approval, which allowed approval of the drug based on Phase 2 trials using surrogate markers (in this case, cytogenetic responses).

As imatinib gained approval as frontline therapy for treatment of Ph chromosome-positive CML, resistance to imatinib became an important issue. Researchers found that this resistance was usually due to mutations in BCR-ABL that interfere with imatinib binding. Two companies therefore designed inhibitors that can bind to and inhibit these resistant BCR-ABL proteins and thus successfully treat imatinib-resistant CML–dasatinib (Bristol-Myers Squibb’s Sprycel) and nilotinib (Novartis’ Tasigna). This is an example of the use of reiterative translational studies to determine mechanisms of drug resistance, and the design of second-generation drugs to combat this resistance. This type of follow-up strategy was discussed in the de Bono and Ashworth article and in our previous blog post.

Only a few years ago, many industry commentators were of the opinion that the development of imatinib to treat CML was a unique case, and development of other personalized biology-driven drug discovery-based cancer medicines would not be successful. However, the examples discussed in the de Bono and Ashworth article (and elsewhere) show that that is not true.

Roche/Plexxikon’s PLX4032

The second example of successful use of the hypothesis-testing clinical trial strategy is the development of Roche/Plexxikon’s PLX4032 for metastatic melanoma. This compound is exquisitely specific for B-Raf carrying the V600E mutation B-Raf(V600E). This is the most common somatic mutation found in human melanomas, and is a “driver mutation” that is particularly critical for the malignant phenotype of human metastatic melanomas that carry the mutation.

We have discussed PLX4032 in three articles on this blog in 2010, published on March 2, March 10, and August 27.

As in the case of imatinib, researchers achieved proof-of-mechanism and proof-of-concept for PLX4032 in a dose-escalation Phase 1 trial in patients who were preselected for carriers of the B-Raf(V600E) mutation. The Phase 1 trial took place in 2008/2009. This was followed by an extension phase in which patients were given the maximum tolerated dose of the drug. Patients showed an 81% response rate (i.e, a partial or a complete response). The estimated median progression-free survival among all patients was over 7 months, as compared to less than 2 months in large numbers of advanced melanoma patients as determined by historical analysis. Oncologists had never seen such a dramatic response in treatment of metastatic melanoma.

PLX4032 is on an accelerated path to potential registration, and parallel Phase 2 and Phase 3 clinical trials are in progress in previously treated and previously untreated patients, respectively, all who have metastatic melanoma carrying the B-Raf(V600E) mutation.

Despite the dramatic regressions and increased survival seen in the Phase 1 trials, all the patients apparently eventually suffered relapses. As stated in the article on PLX4032 in the 30 September 2010 issue of Nature, researchers are therefore doing reiterative translational studies to determine the mechanisms of resistance to PLX4032 in cases of tumor regrowth after treatment with the drug. Proposed strategies include the development of combination therapies that include PLX4032 and other targeted drugs, immunotherapeutic agents, or chemotherapy. Given the promising efficacy and safety profile of PLX4032, researchers believe that the drug has the potential to enable the development of such combination therapies.

In conjunction with the early clinical trials of PLX4032, researchers developed a real-time polymerase chain reaction (PCR) assay to assess B-Raf(V600E) mutation status. The assay has the potential to be used as a companion diagnostic in treatment with PLX4032.  As stated in the 30 September article, researchers are assessing the reliability of the PCR assay In the ongoing concurrent Phase 2 and Phase 3 clinical trials of PLX4032.

A synthetic lethal therapeutic strategy using KuDOS/AstraZeneca’s olaparib

The third example of successful use of the hypothesis-testing clinical trial strategy is taken from Drs. de Bono and Ashworth’s own work. The therapeutic strategy in this example is fundamentally different from the cases of imatinib and PLX4032, both of which are exquisitely targeted drugs that inhibit specific mutated versions of oncogenes. Instead, this example involves the use of synthetic lethality in the design of an anticancer therapeutic strategy. Based on classic studies in yeast and Drosophila, synthetic lethality is defined as a situation in which mutation in either of two genes individually has no effect, but simultaneous mutation in both genes is lethal. In cancer, if one gene in a synthetically lethal pair is defective (and especially if this defect is involved in the malignant phenotype) targeting the other gene with a drug should be selectively lethal to the tumor cells but not to normal cells. If this works, it should result in a large therapeutic window for treatment with the drug.

Women with a germline mutation in one BRCA1 or BRCA2 allele have a high risk of developing breast and ovarian cancer; BRCA1 or BRCA2 carrier status in men also carries an increased risk of developing prostate cancer. Via the process of loss of heterozygosity, cells of carriers of loss-of-function mutations in BRCA1 or BRCA2 can lose the wild-type allele, resulting in cells that lack BRCA1 or BRCA2 function. The products of the two BRCA genes are both involved in the pathway for DNA repair via homologous recombination. Loss of a functional homologous recombination pathway results in the development of genomic instability that can lead to carcinogenesis. Moreover, since BRCA-negative tumor cells cannot repair their DNA via homologous recombination, they are dependent on an alternative pathway of DNA repair, which involves the enzyme Poly(ADP) ribose polymerase (PARP). Since the average cell must repair its DNA thousands of times a day, researchers hypothesized that BRCA-negative tumor cells should be uniquely vulnerable to drugs that inhibit PARP. In contrast, normal cells are able to utilize the homologous recombination pathway, and should not be affected by PARP inhibitors.

Alan Ashworth and his colleagues developed and published this synthetic lethality strategy for therapy of BRCA-negative breast cancer in 2005. They showed that cells deficient in BRCA1 or BRCA2 were about 1,000-fold more sensitive to a class of PARP inhibitors developed by AstraZeneca (AZ) subsidiary KoDOS Pharmaceuticals (Cambridge, MA) than cells with BRCA1 and BRCA2 function. Treatment of BRCA-deficient cells with the PARP inhibitors resulted in chromosomal instability and cell cycle arrest, followed by apoptosis. The efficacy and specificity of the PARP inhibitors for BRCA-deficient cells also carried over to in vivo studies in mouse models. These cell culture and animal studies constituted the generation of a strong hypothesis that this synthetic lethal therapeutic strategy would be useful in developing antitumor treatments for patients with BRCA-negative breast cancer.

In 2006 and 2007, Drs. Ashworth, de Bono, and their colleagues (including researchers from KuDOS and AZ) conducted a Phase 1, hypothesis-testing clinical trial of KuDOS/AZ’s potent, orally-active PARP inhibitor olaparib (AZD-2281; formerly known as KU-0059436). The study enrolled a total of 60 patients with a variety of types of solid tumors, including 22 who were confirmed BRCA1 or BRCA2 mutation carriers and one patient with a strong family history of BRCA-associated cancer but who declined mutation testing. The study was published in July 2009 in the New England Journal of Medicine. The trial was a dose-escalation study–the dose was increased from 10 mg daily for two of every three weeks to 600 mg twice daily. A reversible dose-limiting toxicity was seen in one of eight patients receiving 400 mg twice daily, and in two of five patients who received 400 mg twice daily. Based on these results, the researchers established 400 mg twice daily as the maximum tolerated dose. They then enrolled a new cohort of carriers of a BRCA1 or BRCA2 mutation; these patients received a dose of 200 mg twice daily.

As a Phase 1 trial, the primary objectives were to determine safety, adverse effects, the dose-limiting toxicity and maximum tolerated dose, and the pharmacokinetic and pharmacodynamic profiles. Once these were established, the aim was to test the hypothesis that patients’ BRCA1 or BRCA2 mutation-associated cancers would show an objective antitumor response to olaparib as a single agent. In terms of safety, adverse effects were generally mild. There were two patients deaths due to infectious disease that were deemed not to be drug related. There was also no difference in adverse effect profiles between known BRCA1 and BRCA2 mutation carriers and other patients.

The researchers established three types of biomarkers. The predictive biomarker was the presence of BRCA1 or BRCA2 loss-of-function mutations, as determined by standard sequencing methods in patients with a family history of BRCA-associated cancers. The pharmacodynamic biomarker was the inhibition of PARP enzymatic activity in peripheral blood mononuclear cells and in tumor biopsies taken before and after olaparib treatment, and the formation of double-strand DNA breaks in hair follicle tissue. The intermediate endpoint biomarker consisted of radiological determination of tumor shrinkage and biochemical tests for serum tumor markers.

Using the pharmacodynamic biomarker, the researchers showed that inhibition of PARP was over 90% in peripheral mononuclear cells in patients treated with 60 mg or more of olaparib twice daily. Determination of PARP activity in tumor biopsies before and after 8 days of treatment showed that drug treatment inhibited PARP in tumor tissue. Pharmacodynamic studies in samples of plucked eyebrow hair follicles showed that induction of formation of double-strand breaks occurred within 6 hours of olaparib treatment. These studies constitute proof-of-mechanism of olaparib in humans.

In studies to determine whether olaparib treatment induced antitumor responses, the researchers found that such responses only occurred in patients with confirmed BRCA1 or BRCA2 mutation carrier status, except for one patient who declined mutational testing but had a strong family history of BRCA mutation-related cancer. 23 patents who were confirmed or (in the one case) deemed to be BRCA mutation carriers were treated. Of these 23 patients, two could not be evaluated. Two of the remaining patients had tumors not typically associated with BRCA mutations, and neither received clinical benefits from drug treatment.

Of the remaining 19 patients (who had ovarian, breast, or prostate cancer), 12 exhibited clinical benefits from olaparib treatment, with either tumor responses (determined radiologically or via serum tumor markers) or stable disease for a period of four months or more. Nine BRCA carriers had a tumor response. Eight patients with advanced ovarian cancer had a partial response (determined by radiology), and six of these had a greater than 50% tumor response based on tumor marker assays. Of the three patients with advanced BRCA2 breast cancer, one had a complete remission lasting for over 60 weeks, and another had stable disease for 7 months. The other breast cancer patient, who had refused mutational testing, had a decline in metastases and an over 50% decline in serum tumor markers. The patient with BRCA2-related castration resistant prostate cancer has an over 50% reduction in PSA levels, and resolution of bone metastases. He had been participating in the study for over 58 weeks at the time of the cutoff date, and for more than 2 years since that date.

The above efficacy data constitutes proof-of-concept, and confirms the hypothesis that BRCA-associated cancers can be addressed by a synthetic lethal therapeutic strategy based on the use of the PARP inhibitor olaparib. Olaparib also has a satisfactory adverse effect profile, and lacks the toxicity typically seen with cancer chemotherapy. Since this Phase 1 clonal trial, AZ had taken olaparib into Phase 2 clinical trials in advanced BRCA-related breast and ovarian cancer. Olaparib has continued to demonstrate efficacy and a relatively mild adverse effect profile in these trials, as shown here and here, and as also discussed in a July 2010 Medscape article.

Dr. Ashworth and his colleagues noted that not all cancers in BRCA1 or BRCA2 carriers respond to olaparib. They hypothesize that different BRCA1 or BRCA2 mutations may result in different defects in homologous recombination, which may cause variations in sensitivity to PARP inhibition. Moreover, certain secondary BRCA2 mutations may restore BRCA function, which may cause resistance to PARP inhibition. They see the need to develop assays for homologous recombination proficiency, which might be used in reiterative translational studies to determine causes of resistance to olaparib.

Synthetic lethal therapy with PARP inhibitors such as olaparib may be applicable to other types of cancers that have defects in DNA repair by homologous recombination. These may include sporadic breast and ovarian cancers that acquire loss of function of BRCA1 or BRCA2 via somatic genetic or epigenetic events, and other sporadic cancers that develop loss of function (via somatic genetic or epigenetic events) of other proteins involved in the homologous recombination DNA repair pathway.

Dr. Ashworth and his colleagues have also shown that loss of function of DNA damage signaling proteins (e.g., ATM, ATR, CHK1, CHK2), and of Fanconi anemia proteins, can induce sensitivity to PARP inhibition. Loss of function in these pathways may be relatively common in other sporadic cancers. It will be essential to develop biomarkers for loss of function of these DNA repair proteins in order to design hypothesis-testing clinical trials to investigate the potential of olaparib (or other PARP inhibitors) to treat this broader class of cancers.

As show by these three examples–and the other examples discussed in the 30 September 2010 de Bono and Ashworth Perspective (see Box 5 in that article)–researchers have been using the personalized medicine hypothesis-testing strategy to develop exciting new oncology drugs to treat disease in specific classes of patients. However, except for the case of imatinib, all of the drugs are still in clinical trials and have not yet achieved registration, which is the real test of the success of this strategy. Moreover, as we discussed in the first article in this series, the personalized medicine hypothesis-testing strategy is a work in progress. For example, biomarker identification and qualification/validation, which is a critical need for further development and utilization of this new clinical trial strategy, is an early-stage area of science and technology. Nevertheless, the personalized medicine hypothesis-testing strategy for cancer drug development provides a means to extend biology-driven drug discovery into the clinic, to decrease the time and cost of clinical trials, and to develop anticancer drugs that should be superior to both conventional chemotherapy and to early-generation targeted drugs.


As the producers of this blog, and as consultants to the biotechnology and pharmaceutical industry, Haberman Associates would like to hear from you. If you are in a biotech or pharmaceutical company, and would like a 15-20-minute, no-obligation telephone discussion of issues raised by this or other blog articles, or of other issues that are important to  your company, please contact us. We also welcome your comments on this or any other article on this blog.

14 October 2010

Translational research in cancer makes a big splash in Nature (Part 1)

By |2018-09-12T21:41:38+00:00October 14, 2010|Biomarkers, Cancer, Drug Development, Drug Discovery, Personalized Medicine, Strategy and Consulting, Translational Medicine|

The 30 September issue of Nature included a major emphasis on translational research in cancer. Featured articles included an editorial, a Perspective, and a research report. There was also an online “Specials” archive on translational cancer research, containing many recent research reports, all with free access to nonsubscribers.

The theme of these articles was the development of novel strategies for accelerating the translation of research on cancer biology into safe and efficacious therapies.

The Perspective, entitled “Translating cancer research into targeted therapeutics”, by British researchers Johann S. de Bono, M.D., Ph.D. and Alan Ashworth, Ph.D., outlines a novel disruptive clinical trial strategy for accelerating the translation of biology-driven oncology drug discovery into the clinic, with an early determination of proof of concept. This new strategy is designed  to ameliorate the high levels of Phase 2 and Phase 3 attrition of cancer drugs, as well as to lower the cost of clinical trials and to shorten the time from preclinical studies to the approval and marketing of oncology drugs that successfully emerge from clinical trials. It also is designed to aid in the development of therapies that provide greater patient benefit (in terms of progression-free survival) than the typical new oncology drugs that reach the market.

We have discussed clinical trial strategies of this type in two of our publications–our 2009 book-length report Approaches to Reducing Phase II Attrition (available from Cambridge Healthtech Institute, and our 2009 article published in Genetic Engineering and Biotechnology News, “Overcoming Phase II Attrition Problem” (available on our website). The de Bono and Ashworth article provides a more detailed and specific presentation of this strategy with respect to oncology drug development, and provides several examples of its successful application.

In the traditional Phase 1/Phase 2/Phase 3 format of cancer drug development, clinical studies focus on treating patient populations with advanced cancers that have not been characterized in terms of their genetics and molecular biology. The trials culminate in large, pivotal randomized Phase 3 trials that typically last several years, and are aimed at regulatory approval. In most cases, new drugs that emerge from Phase 3 trials and win approval only improve survival by a few months. Patients who participate in Phase 1 and Phase 2 clinical trials usually derive little or no benefit, and a high proportion of drugs fail in Phase 2 or Phase 3. This clinical trial format determines how well a particular drug or drug combination works for the average patient. However, that treatment might not be the best for a given individual patient.

As basic researchers have advanced the study of molecular genetic pathways of cancer, drug discovery researchers have been developing targeted therapies for cancer. These drugs–such as monoclonal antibodies (MAbs) like trastuzumab (Genentech/Roche’s Herceptin) and kinase inhibitors like imatinib (Novartis’ Gleevec/Glivec) work by modulating specific biomolecules (e.g., overexpressed or mutated oncogenic proteins) that are critical for the malignant phenotype. Population-based clinical trials of unselected patients make little or no sense in developing targeted therapies. Instead, clinical researchers need to first select groups of patients whose cancers express the biomolecule to be targeted, and preferably whose cancers are driven by that particular biomolecule. This way of thinking leads to the formulation of a new clinical trial strategy, as outlined in the Perspective.

Drs. de Bono and Ashworth call this strategy a personalized medicine (or “stratified medicine”) hypothesis-testing approach. The first step in this strategy is to develop a strong biological hypothesis that a particular altered molecular target is critical for the malignant phenotype of a particular cancer. This hypothesis is usually generated as the result of laboratory and clinical studies. Researchers need to show that blocking of the function of the altered target results in a lethal or cytostatic effect in cancer cells that express the the target, but not in normal cells that do not. It is also preferred that resistance to agents that block the target is not easily gained.

In the discovery stage of this strategy, researchers need not only to identify and optimize clinical candidate drugs that modulate the target, but also biomarkers that can be used to identify patients whose tumors express the altered target and are therefore likely to benefit from treatment. These biomarkers are called “enrichment biomarkers”, and have the potential to become predictive biomarkers. (Predictive biomarkers may also be the basis for the development of companion diagnostics). It is also important to identify pharmacodynamic biomarkers (biomarkers that can be used to determine target occupancy by the drug) and intermediate endpoint biomarkers (which can assess antitumor activity of the drug–for example, radiological assessment of tumor regression). Identification and qualification/validation of biomarkers is a work in progress, and is a critical need for further development and utilization of the personalized medicine hypothesis-testing clinical trial strategy.

Once targets and drugs that modulate them have been identified, they must be validated in animal models. The issue of the inadequacy of current mouse models of cancer–mainly xenograft models in which human cancer cell lines are transplanted into immune deficient mice–to predict drug efficacy is important both in the traditional cancer drug development strategy and in the novel strategy discussed in the de Bono and Ashworth article. We have discussed development of improved animal models for cancer drug development in an earlier blog post. de Bono and Ashworth note that there is an urgent need to develop such improved animal models. Nevertheless, as discussed in the de Bono and Ashworth article, there are examples of the successful implementation of the personalized medicine hypothesis-testing strategy of cancer drug development that have used traditional animal models in the preclinical phase.

In the personalized medicine hypothesis-testing strategy, clinical trials have the same three phases as in traditional trials. However, the trials involved stratification of patients using biomarkers, such that clinical studies are done in patients whose tumors express the target of the drug. Trial design is also more flexible and adaptive, and is contingent on obtaining key clinical data. The trials focus on determining the following as early as possible:

  • Proof of mechanism: determining a dose range and dosing schedule under which the drug achieves sufficient target occupancy for long enough, using biomarker-based pharmacodynamic assays.
  • Proof of concept: Determining that once sufficient target occupant is achieved, the drug exhibits antitumor activity, as determined using intermediate endpoint biomarkers.

In first-in-human clinical trials, in addition to determining safety and tolerability and evaluating pharmacokinetics and pharmacodynamics as in traditional Phase 1 trials, researchers also pursue rapid dose escalation, until proof of mechanism is achieved, using the appropriate biomarkers. Researchers then move on to proof of concept hypothesis testing, at doses and dosing schedules (ideally, the maximum tolerated dose) that are sufficient to address the target for long enough to have a biological effect. Ideally, researchers should move seamlessly from determination of proof-of-mechanism to assessment of antitumor activity, via adaptive trial design and patient selection using enrichment biomarkers.

If the above early-stage strategy results in a strong determination of proof of concept, this provides the basis for moving on to Phase 3 trials in patients selected using enrichment/predictive biomarkers, with the goal of drug registration. Such Phase 3 trials should have a higher probability of success than traditional Phase 3 trials in unselected patient populations, with less than adequate demonstration of proof of concept in Phase 2.

However, in the personalized medicine hypothesis-testing strategy, there is also the need for reiterative translational studies, between the laboratory and the clinic and back to the laboratory. Such studies should be designed as early as possible in clinical development. For example, clinical trials might allow researchers to obtain tumor samples to determine mechanisms of drug resistance. Such studies might form the basis for generating further hypotheses that are relevant to reversing drug resistance, via such means as development of combination therapies or of second-generation drugs.

The personalized medicine hypothesis-testing strategy is a work in progress. However, as we shall discuss in Part 2 of this series, there are examples of its successful implementation. And this strategy provides a means to extend biology-driven drug discovery, arguably the most successful drug discovery strategy of the past decade, into early and mid-stage clinical trials, thus increasing the probability of clinical success.

Nevertheless, it must also be emphasized that our understanding of disease biology (especially cancer biology) is limited, thus limiting our ability to successfully carry out biology-driven drug discovery in all cases. However, as our understanding of disease biology grows–in an incremental manner–as the result of basic research mainly in academic laboratories, we should be able to utilize this research to develop novel, breakthrough treatments via biology-driven drug discovery and personalized medicine hypothesis-testing clinical trials.

2 October 2010

Knockout rats made “the good old way”

By |2018-09-12T21:42:54+00:00October 2, 2010|Animal Models, Drug Development, Drug Discovery, Neurodegenerative Diseases, Psychiatric Diseases|

In Chapter 7 of our March 2010 book-length report, Animal Models for Therapeutic Strategies (published by Cambridge Healthtech Institute), we discussed recently-developed methods for producing knockout rats. These methods included zinc-finger nuclease (ZFN) genome editing and transposon mutagenesis in cultured spermatogonial stem cells. Our most extensive discussion was of the ZFN editing technology, which was developed by Sangamo BioSciences (Richmond, CA), and is the basis of the knockout rat models marketed by Sigma-Aldrich Advanced Genetic Engineering (SAGE). We also mentioned the SAGE knockout rat platform in an earlier blog post.

In Chapter 7 of our report, we also mentioned that it would now also be possible to construct knockout rats “the good old way”–using the same homologous recombination technology that researchers use to create knockout mice. Drs. Mario R. Capecchi, Martin J. Evans and Oliver Smithies were awarded the Nobel Prize in Physiology or Medicine for 2007 for having developed this technology in the late 1980s. To construct knockout mice, researchers isolate and culture mouse embryonic stem (ES) cells. These are derived from the inner cell masses of preimplantation mouse blastocyst embryos, and grown under particular culture conditions. These cells are subjected to homologous recombination with a vector containing a truncated version of the gene to be targeted, to eventually yield knockout mouse strains.

It has not been possible to develop knockout rats because the conditions for culturing ES cells worked only for a few inbred mouse strains, and not at all for either most mouse strains or for the rat. Conditions for culturing mouse ES cells are complex. They involve the use of feeder fibroblasts and/or the cytokine leukemia inhibitory factor (LIF), together with selected batches of fetal calf serum or bone morphogenetic protein (BMP). These culture conditions had been determined empirically.

In 2008, Dr. Austin Smith (Director of the Wellcome Trust Centre for Stem Cell Research, University of Cambridge [Cambridge, UK]) and his colleagues developed culture conditions that allowed them to culture rat ES cells that were capable of transmitting their genomes to offspring. These ES cells could also be used to produce knockout rats.

Dr. Smith and his colleagues realized that the standard conditions for culturing mouse ES cells expose the cells to inductive stimuli (e.g., fibroblast growth factor 4 [FGF4]), which can activate ES cell commitment and differentiation. The aim of ES cell culture is to expand the cell population while maintaining pluripotency.  The researchers therefore cultured rat ES cells with leukemia inhibitory factor (LIF)-expressing mouse fibroblast feeder cells, in a medium containing two or three small-molecule inhibitors of pathways involved in ES cell commitment and differentiation, plus human LIF. (LIF supports proliferation of ES cells in an undifferentiated state.) This medium is known as 2i (for 2-inhibitors) or 3i medium.

Rat ES cells cultured in this manner expressed key molecular markers found in mouse ES cells. They also, when injected into blastocysts, can give rise to chimeric rats; i.e., they transmute their genomes into offspring. Such cultured rat ES cells thus are capable of being used to construct knockout rats.

In the 9 September 2010 issue of Nature, Dr. Qi-Long Ying (University of Southern California, Los Angeles CA) and his colleagues published the first study describing construction of a knockout rat strain via homologous recombination. (Dr. Ying, then at the University of Edinburgh, had been on the team led by Austin Smith that developed culture methods for rat ES cells.) This rat strain is a p53 gene knockout. The researchers designed a targeting vector to disrupt the p53 tumor suppressor gene via homologous recombination; the vector allowed for antibiotic selection for cells that had been successfully targeted. They transfected this vector into rat ES cells cultured in 2i medium, performed the antibiotic selection, and cultured the resistant cells. These cells were shown to have one of their two (since they were diploid) p53 genes disrupted. The researchers were able to routinely generate p53-targeted rat ES cells by this method. They also injected p53-targeted rat ES cells into rat blastocysts, transferred the blastocysts into pseudo-pregnant female rats, and obtained chimeric offspring. However, in the first studies, the p53-targeted rat ES cells exhibited low germline transmission efficiency.

In the mouse system, the failure of cultured ES cells to contribute to the germline is often caused by chromosomal abnormalities in the ES cells. This was also the case with the rat ES cells. In the case of mouse ES cell culture, cells with chromosomal abnormalities have a selective growth advantage over those with normal karyotypes. The smaller, slower-growing mouse ES cell clones tend to have normal karyotypes, and to give improved germline transmission. The researchers therefore subcloned their p53 gene-targeted rat ES cells, and selected for small, slower-growing subclones. These rat ES cell subclones were euploid. When injected into blastocysts, these rat ES cell clones gave rise to chimeric rats that the researchers further bred to generate homozygous p53 gene-targeted (i.e., p53 knockout, or p53 homozygous null) rats.

Using these methods, it should be possible to generate knockout rats for other genes routinely, including sophisticated knockouts such as tissue-specific gene knockouts.

Meanwhile, SAGE has generated p53 knockout rats, using its ZFN technology. As with the original p53 knockout mice, these rats develop normally, but are prone to development of spontaneous tumors. p53 knockout rats generated via homologous recombination should also be susceptible to spontaneous generation of tumors. However, as yet no data has been published. It remains to be seen which of these systems–p53 knockout mice or p53-knockout rats generated via either homologous recombination or ZFN editing, will be most useful in basic cancer research, or in such applications as carcinogenicity screening of compounds.

Why is the ability of researchers to generate knockout rats, as opposed to knockout mice, so important? The anatomy and physiology of the rat is closer to humans than is the mouse. There are also many rat models of complex human diseases (especially cardiovascular and metabolic diseases) that are better disease models than those based on inbred mouse strains. In addition, the larger size of the rat facilitates experimental procedures that involve surgery, getting blood samples for analysis, or isolation of specific cell populations. Researchers usually prefer rats over mice for physiological and nutritional studies, studies of psychiatric diseases, and in cases when a particular rat disease model is more applicable to a project than mouse strains. The rat is also widely used in preclinical efficacy and safety studies.

With respect to models for central nervous system (CNS) diseases, gene-targeted and transgenic rat models may be expected to be better than mouse models. The rat is more intelligent than the mouse, and has a bigger brain. Unlike mice, rats are sociable and easily trained. Moreover, there are some new rat models of cognition, which enable researchers to perform studies that they previously thought could only be done in nonhuman primates. And optogenetics technology, which allows researchers to engineer specific neurons so that their activity can be switched on or off with laser light, in order to dissect the role of these neurons in behavior, is being implemented in rats. These new developments, together with knockout and transgenic technologies, should allow researchers to develop new rat models of psychiatric diseases, as well as of neurodegenerative diseases such as Alzheimer’s and Parkinson’s diseases. The lack of good animal models is a major factor in the high clinical attrition rate of CNS drugs, so new models are needed. There are of course no guarantees that novel rat models will help lower CNS drug attrition rates, but it is well worth trying these new approaches.

As we also discussed in Chapter 7 of Animal Models for Therapeutic Strategies, researchers are also interested in developing animal models based on mammalian species other than the mouse and the rat. We discussed methods for gene targeting by recombinant adeno-associated virus (rAAV) in pigs and ferrets in that chapter. In principle, ZFN editing technology could be also used to generate gene knockouts in mammalian species other than rodents. Moreover, the type of research done in the rat by Austin Smith, Qi-Long Ying, and their colleagues might be applied to developing culture conditions for ES cells of other mammalian species, which could set the stage for developing gene knockouts in these species via homologous recombination.

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