Immuno-oncology is an exciting but challenging subspecialty that falls underneath the umbrella of oncology research and development. Unlike more traditional oncology treatments that target the tumor itself (albeit sometimes rather nonspecifically), immuno-oncology therapies attempt to harness the power of an individual patient’s immune system to combat cancer. To use an automotive metaphor, immuno-oncology therapies modulate the immune system by “releasing the brakes” or “stepping on the gas” or both. In other words, the goal of immuno-oncology therapies is to stimulate the immune system to recognize and subsequently attack the cancerous cells, either by removing a block (often perpetuated by the cancer itself) to a particular immune pathway that is not otherwise active and/or by intensifying a response that is already active but is insufficiently robust to defeat the cancer without a boost. Although the field is still in its early stages, these treatments have shown great promise in increasing survival rates and a few of these therapies have been FDA approved. However, there are still many questions about safety and efficacy that remain unanswered and a number of drug development hurdles that make answering these questions difficult.
The Lack of Translatable Animal Models
One of the biggest challenges to developing effective immuno-oncology therapies is determining the appropriate starting dose for first-in-human clinical trials. Due to the inherent risks associated with most oncology therapies, with immuno-oncology being no exception, human trials generally begin in patients rather than healthy volunteers. Because these individuals are already sick and failure to achieve a meaningful and timely clinical response can lead to disease progression or even death, reaching an effective dose as quickly as possible is critically important. However, there are a number of factors that make determining the appropriate dose and dosing schedule challenging, many of which are related to a lack of truly translatable animal models of efficacy and safety that are capable of accurately predicting immuno-oncology outcomes in humans.
The primary roadblock to generating appropriately robust animal models for immuno-oncology therapies is that the immune system is both complex and diverse across animal species, making interpretation of the pharmacodynamic response and toxicity difficult. In many cases, these molecules may be specifically designed for the human target(s) and have poor cross-reactivity with the analogous protein or pathway in animals. Even in humanized models (i.e., those that have had human immune cells/tissues introduced in an attempt to generate a more human-like immune response), observed outcomes do not necessarily tell the whole story of what will happen in the context of a human being.
The Need for Predictive Biomarkers
Another important issue is that to date, the field has struggled to identify predictive biomarkers of response in both animal models and humans. The primary challenge in this regard is a genuine lack of understanding of both the complex mechanisms of cytokine signaling and the timing of the phases of immune cell activation. Because of this, there is often little choice but to monitor tumor shrinkage or long-term survival as the primary efficacy endpoint. However, tumor shrinkage can present its own challenges to the ability to accurately interpret clinical response, as an effective immune response can often cause the tumor to appear more active or even larger, at least in the short term, due to a phenomenon call pseudo-progression. Pseudo-progression can occur due to T-cell infiltration of the tumor (a desirable effect of immuno-oncology therapy), which can be misleading with tumor imaging. In the end, the lack of short-term, predictive biomarkers can make identification of an effective dose or dosing schedule a daunting task for any clinical development team.
The Presence of a Non-Traditional Dose-Response Relationship
Another challenge is that, so far, many approved immuno-oncology therapies have failed to produce a traditional dose-response curve and have relied on other metrics for selection of dose(s) to advance into registration trials. While these therapies have obtained FDA approval based in part upon evidence of efficacy in certain patients, there remain many patients who failed to respond to these therapies, with little information as to why. As a result, many questions remain unanswered, especially about how best to deploy these therapies in patients who display a more muted or less durable response using established dosing regimens. New research in areas such as T-cell exhaustion, which can result from over-activation of the immune system, may offer some help by informing the refinement of immuno-oncology doses or dosing schedules and by suggesting potential add-on therapies that might make the immuno-oncology response more robust.
As new immuno-oncology therapies are being explored in clinical trials along with a seemingly unlimited set of potential immuno-oncology combinations with traditional oncology therapies and other immuno-oncology molecules, it is more critical than ever to continue to build our knowledge in this complex and growing field.