In recent years, we’ve seen a surge of investment in immuno-oncology (IO) drug development, despite there being a number of unique challenges to face in this field. One particular challenge sees developers having to contend with a highly complex tumour micro-environment (TME), which is incredibly hard to represent with preclinical in vitro models. As a result, many companies struggle to translate preclinical data into accurate predictions of clinical effects, leading to a high risk of failure in late-stage development.
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So, how can you ameliorate this risk? A powerful approach is to collect a comprehensive data package from human cells using assays that have been specifically designed to reflect the TME. Here, we explain the importance of building a human immunology data package, outline the main strategies to achieve this, and describe how you can use these results to boost your IO drug development success.
Why is a comprehensive human immunology data package so valuable in immuno-oncology drug development?
In IO drug development, it’s essential to model the clinical in vivo environment as closely as possible. This is because drug efficacy is often highly dependent upon the context of the TME. For example, certain signalling pathways may have a greater or lesser effect on the environment of a tumour, depending on the delicate balance between the immune system and the developing cancer.
While there are several mouse models available to mimic the clinical situation, significant differences between the human and murine immune systems mean that it’s hard to gain translatable insights from these models. Therefore, it’s crucial to back up this in vivo data with extensive in vitro data from human cells.
However, since the TME is the net result of multiple cellular and molecular interactions, it’s an incredibly complex environment to represent in vitro. What’s more, it will be continually evolving within each tumour, and will therefore be subtly different between individual patients.
Given these complexities, it’s not surprising that no single in vitro model can fully reflect the clinical situation. Therefore, to reliably predict clinical effects, you’ll need to build an extensive package of data using the most physiologically relevant models and assays. Then, if you’re able to leverage in-depth immunological expertise, you can piece together the puzzle to infer your drug’s likely clinical effects.
When you’re developing your suite of assays, there are a number of strategies you can employ to make your data as clinically relevant as possible. One such approach is to ensure that the immune cells in your models are showing the characteristics they would exhibit in a tumour, so that the insights you gain from your studies are more relevant to the clinic.
Generating cells in the most relevant functional state for immuno-oncology drug development
While you can gain useful initial data using immune cells from healthy donors, there are several reasons why these populations won’t be representative of those found in a tumour. Firstly, the proportions of certain cell types will be different – for example, regulatory T cells can be significantly enriched in the TME compared to peripheral blood.
Secondly, many immune cells will show distinct functional characteristics in a tumour. For example, T cells are often tolerant or exhausted, and tumour-associated macrophages differ drastically from monocytes found in healthy blood samples.
To overcome these issues, you can employ in vitro polarisation and activation protocols to generate cell populations more representative of those in the TME. In fact, these protocols could make a real difference to the value of your data, as they could significantly influence the expression of your target. For instance, many second-generation checkpoint targets aren’t expressed by T cells in steady state but are expressed dynamically following activation. Therefore, if you’re studying such targets, it’s crucial to activate your T cells in order to gain physiologically relevant data.
By using appropriate protocols to generate cells in the relevant functional state, you can build suitable assays to evaluate your drug’s action in relation to the immunosuppressed TME. These assays could include individual cell types in monocultures, more complex co-cultures, and cultures including tumour cells to act as immune-modulators and as targets for killing. Taken together, these assays can provide a range of complementary insights into the effects of your drug on the human TME.
With a panel of these in vitro assays, you can begin to formulate your human immunology data package. However, these cultures alone will not be enough to replicate the complexities of the TME – it’s also crucial to consider the structureof the environment.
Modelling the 3-D tumour environment in immuno-oncology drug development
Co-culturing isolated immune cells with tumour cells is essentially a 2-D assessment, so does not reflect the histological structure of the environment in vivo. Therefore, 3-D analyses are becoming increasingly popular as more physiologically relevant options.
A useful strategy here is to use tumoroids or precision-cut tumour slices generated from fresh patient material. Indeed, assays using this material can provide extremely powerful data, as they can be highly representative of the situation in the TME.
However, these 3-D analyses do have some drawbacks. Firstly, since there will only be a limited amount of tissue available from a given tumour, they can’t support large-scale screening. What’s more, it’s impossible to standardise the immune cell complement in tumoroids or slices, which means that it’s not possible to control all variables between samples, restricting the conclusions that can be drawn from these models.
Taken together, these issues do limit the use of 3-D analyses to some extent. However, if you use these assays alongside 2-D cultures, you can gain valuable supporting data to add to your human immunology data package.
The power of a comprehensive human immunology data package in immuno-oncology drug development
Ultimately, by designing your preclinical in vitro studies to use the most physiologically relevant assays, you can build an extensive suite of human immunology data that reflects the situation in the TME. Then, to gain the maximal value from this information, you’ll need to employ extensive immunological expertise to interpret it in the context of your preclinical in vivo data. It’s essential to consider these different types of data in combination, since information from mouse models will more closely reflect the in vivo environment, whereas in vitro human immunology data can provide a better insight into human immune interactions. Ultimately, by leveraging a thorough understanding of immunology to interpret these datasets alongside each other, you’ll have the best possible confidence in translating your preclinical findings into reliable predictions of clinical effects.
While developing a comprehensive human immunology data package is crucial for your success in IO, there are many more elements to a successful drug development programme. If you’d like to learn more about how to navigate the challenges of this field, download your free eBook: “Immuno-oncology: Five Tactics to enhance your Drug Development Strategy.”