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

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.

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