In 2011, Plexxicon and Roche published the results of a trial on vemurafenib in patients with BRAF V600E mutant late-stage melanoma. The effect on progression-free survival was exciting and offered hope for many patients and subsequently, the drug was approved by the FDA. But while the drug offered many patients an extra window of time, they eventually became resistant to the therapy and relapsed.
This story is all too familiar to oncologists and biopharma researchers developing new cancer treatments. And while immunotherapy approaches now appear to offer longer respite for some patients, even these are not a silver bullet.
So as a pharma or biotech researcher working in cancer drug development, how do you reduce the chance of tumour drug resistance developing and ensure prolonged efficacy and return on investment for your cancer therapy? Improving the predictivity of your preclinical drug development programme is the place to start.
In this blog post, we’re sharing 4 top tips for improving your preclinical drug development studies to help reduce the risk of tumour drug resistance developing in the clinic.
1. Use physiologically representative preclinical cancer models
Most tumours are not made up of just a single clone; instead as a tumour evolves over time, subclones emerge with different genomic, molecular and epigenetic characteristics in response to changing selection pressures. The challenge is that just a single subclone could harbour a mechanism of resistance or have the potential to develop one over the course of treatment. This poses a big problem for preclinical cancer drug developers – if there are hundreds of different subclones within a tumour, how can you accurately capture such complex heterogeneity in a preclinical cancer model and be confident that your cancer therapy will be effective in patients?
There is no better place to sample tumour heterogeneity than from a native tumour itself. Patient-derived xenograft (PDX) mouse models created from tumour biopsy samples offer valuable insight into the different subclones that exist within a tumour in vivo and are a great preclinical cancer model for more accurately predicting if resistance is likely to develop in response to treatment, ahead of investing in costly clinical trials. Furthermore, recent advances in humanising patient-derived xenograft mouse models are enabling researchers to model the influence of the tumour microenvironment in the development of drug resistance, which is particularly important when assessing the efficacy of immunotherapy candidates. For example, scientists have recently generated mouse models with humanised bone and peripheral blood mononuclear cells to help pharma and biotech researchers better predict the efficacy of immunotherapies during preclinical drug development.
2. Identify specific patient subgroups that demonstrate treatment efficacy
A key strategy pharma and biotech researchers can use is identifying a specific subgroup of patients during the preclinical stages in which a therapy is likely to be effective. By targeting a therapy to a particular patient subpopulation whose tumours display specific genomic and molecular characteristics, it increases the likelihood that the therapy will be effective in the clinic for this subgroup of patients.
To assist this precision medicine-based approach during preclinical drug development, a team of researchers in the Netherlands developed an ex vivo technique for culturing slices of prostrate and bladder tumours as a method for assessing the efficacy of cancer treatments in different patients. In addition, a clinical study called MATCH-R is helping pharma and biotech researchers developing next generation therapies for patients who have acquired resistance to first-line treatment. The team are creating individual PDX models for 300 cancer patients who have developed resistance to targeted molecular therapies to aid in the development of novel therapeutics for patients who acquire resistance to standard-of-care treatment.
3. Consider combination therapies to enhance tumour sensitivity
Another approach for combating resistance is looking at ways to counteract the mechanisms that develop, such as by using combination therapies, rather than simply failing a candidate if resistance is observed during the preclinical stages. Drug combinations have a number of advantages over monotherapies; a combination can help block off more than one component of the growth signalling pathways that drive tumour cells, while the potential for synergistic effects means that lower dosages of each drug are required, thus reducing the chemotoxicity.
By unravelling mechanisms of drug resistance, researchers can see if a suitable drug is already available that could be given in combination with the new therapy to overcome the resistance mechanism and improve treatment efficacy. However, while researchers have tried to pre-empt resistance mechanisms by combining therapeutic agents targeting key signalling pathways, this hasn’t always been successful due to our incomplete understanding of the complexity of cellular signalling.
One approach is to test every possible DNA mutation and identify those that confer resistance to the drug being tested. A team from the Wellcome Trust Sanger Institute demonstrated how this could be used to map point mutations that confer resistance to the anti-EGFR antibody Cetuximab (Erbitux, Eli Lilly / Merck) in colorectal cancer cells. Along with known resistance mechanisms, this study identified novel MAP2K1 mutations that were confirmed in samples from patients that had relapsed on Cetuximab.
Similarly, by conducting genomic analysis of patient-derived colorectal cancer organoids treated with the immunotherapy cibisatamab, researchers at The Institute of Cancer Research and The Royal Marsden NHS Foundation found elevated WNT/β-catenin pathway activity in resistant organoids. This approach is already showing promising results as the team was able to uncover a way of counteracting this resistance by administering WNT pathway-targeting drugs in combination with cibisatamab to increase immunotherapy sensitivity.
4. Assess drug resistance over more clinically relevant timeframes
Preclinical drug development studies are normally run to tight timelines and therefore pharma and biotech researchers are usually under pressure to assess the potential for drug resistance to develop in just 14-28 days. But most therapies are usually administered in the clinic over several months! To provide peace of mind that only truly promising candidates are moved into clinical trials, pharma and biotech researchers need to ensure that more clinically relevant timeframes are incorporated when planning preclinical drug development programmes, such as assessing the potential for drug resistance to develop over 2-3 months.
Developing an effective cancer therapy to which resistance will not emerge is an ongoing battle for cancer drug developers. However, to maximise the chances of a therapy making it to market, pharma and biotech should first look to enhance key aspects of their preclinical drug development programmes. By using more physiologically representative preclinical models, identifying which patient subgroups respond best to the therapy, looking for potential combination therapies to counteract resistance mechanisms and assessing drug resistance over more clinically relevant timeframes, researchers can help to reduce the risk of drug resistance developing once a therapy hits the clinic.
However, cancer is an incredibly complex disease and undoubtedly will continue to throw new challenges our way – just take the recently uncovered phenomena of tumour hyperprogression, for example. As a cancer drug developer, you have to be prepared to tackle the unexpected and, in order to do so, you need to be armed with the latest cancer models and technologies available that can help you accurately predict the efficacy of your cancer therapy during preclinical development. Fortunately, there is a leading online directory that catalogues cancer models available from preclinical oncology contract research organisations (CROs) around the world to ensure you can always find the right preclinical model for the question you need to answer.
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