Drug resistance remains one of the most pressing challenges in oncology drug development, as it is the leading cause of treatment failure. As researchers look to understand and overcome drug resistance, they need precise, controllable, and scalable models that allow them to isolate and study resistance mechanisms effectively. This article explores the role in vitro models are playing in the fight against drug resistance and explains how they can accelerate the development of successful novel therapies.
The role of in vitro models in drug resistance research
Drug resistance in cancer is a multi-faceted and ever-evolving phenomenon in which multiple mechanisms play different roles and often coexist within one tumor. Tumor heterogeneity, the immune system, and interactions within the tumor microenvironment (TME) are just some of the factors that add to this complexity, making research into drug resistance challenging. To effectively study these multi-layered resistance mechanisms, researchers must utilize a range of precise, scalable systems that facilitate high levels of control.
Cell line models are one of the most commonly used pre-clinical models as they are relatively inexpensive and easy to repeat and scale. This means they can support the high-throughput screening required to investigate the molecular or genetic characteristics that drive resistance mechanisms and screen potential drug candidates in the early stages of pre-clinical research. Cell lines provide substantial information on cell biology as well as drug sensitivity and have contributed to discovery of ATP-binding cassette (ABC) transporters, considered the most important mediator of drug resistance in cancer cells. In fact, there is increasing agreement that the use of these large-scale cell panels is improving the overall value of pre-clinical results.
Organoids are three-dimensional cell cultures that can be derived from patient samples, patient-derived xenograft (PDX) models or murine tumor tissues. Organoid models are highly physiologically relevant as they retain the morphology of the original tumors as well as genetic features like mutations. Additionally, models can be developed quickly from large panels featuring multiple cancer types. Unlike cell line models, organoids preserve tumor heterogeneity, so they mimic the cell diversity and drug sensitivity variations seen in tumors. Consequently, they can effectively predict drug responses in patients and can be used to validate findings from earlier cell line-based studies.
Inducing resistance in in vitro models
Taking tissue from clinically treated patients who have relapsed during treatment is useful as the models that can be created from this tissue reflect actual resistance mechanisms. However, the availability of these samples is limited, so alternative strategies are needed to mimic the resistance phenotype. There are several options available:
Drug-induced resistance models
These models are created by exposing cancer cells to therapeutic agents until they develop resistance. This can be achieved through different methods: continuous exposure to increasing drug concentrations, pulsed treatment where exposure is alternated with recovery periods, or a one-off exposure to high concentrations of the drug. These cost-effective methods can reveal novel and complex mechanisms, allow researchers to induce potential on- or off-target mechanisms of resistance, and mimic aspects of the way resistance develops in patients. Additionally, using drug-induced models means the development of resistance can be studied over time and biomarkers can be compared before and after resistance develops.
However, drug-induced resistance can take time to develop and results can vary; following specific protocols does not guarantee that desired resistance phenotypes will develop. Additionally, while drug-induced models can shed light on complex forms of resistance where multiple mechanisms play a role, they can also make isolating one specific mechanism more challenging.
Engineered resistance models
Engineered resistance models are created by modifying preclinical models with specific genetic alterations seen in drug-resistant tumors. This is achieved with techniques like CRISPR-mediated gene editing, which allows researchers to change the individual genes believed to drive drug resistance. Specific examples include knock-in cell lines which recapitulate relevant disease mutations so gene or mutation phenotype function can be understood, and knock-out lines which enable the assessment of gene function via targeted deletion.
Employing these techniques means specific genetic alterations can examined quickly in well-characterized models which consistently demonstrate the desired resistance phenotype. However, by focusing study on specific resistance mechanisms, it is less likely that novel or complex mechanisms will be revealed. Additionally, as this resistance is created “artificially” rather than induced via exposure to therapeutics, it may not behave in the same way as resistance seen in patients, potentially making any results less relevant to clinical studies.
Just as with all preclinical models, the relative strengths and limitations of engineered or drug-induced resistance models must be considered when studies are being designed. Rather than relying solely on one model type, researchers often utilize both options to further validate their findings and build confidence before moving to the next stage of pre-clinical study.
Workflow integration: bridging in vitro and ex vivo insights
Using multiple models and tests helps researchers build up a more complete picture and better understand treatments and patient populations. Rather than relying on a single model, integrating a “toolbox” of different models into broader workflows and holistic strategies is advantageous. By taking this approach, researchers can leverage the strengths of different model types, whether that’s evaluating novel treatments or identifying specific resistance mechanisms.
While cell lines and organoids provide key insights during preclinical drug development, they cannot recapitulate the complex TME or be used to evaluate the role of the immune system in cancer treatment and resistant. Utilizing ex vivo models (like a 3D ex vivo patient tissue platform) provides a highly clinically relevant platform that can recapitulate the complexity and heterogeneity of human tumors to more accurately predict tumor responses to drug candidates. Complementary models that utilize the same cell line can each play a distinct role within a wider, strategic approach allowing mechanisms of resistance to be hypothesized and validated so researchers can make more informed decisions about which drug candidates should be progressed to clinical stages.
One particularly important application of these integrated workflows is biomarker discovery. In vitro models can help identify certain molecular signatures of resistant cells, but some resistance mechanisms only emerge in the presence of the TME or immune components. By using multiple models, biomarkers that have been detected in various platforms can be analyzed and validated, so resistance mechanisms that might be missed by a single model can be captured. In this way, a more robust diagnostic approach can be reinforced, helping build success in the later stages of drug development.
This approach can be further enhanced with complementary multiomic profiling, which brings together fields such as genomics, proteomics, and metabolomics, so research teams can better understand complex networks of resistance mechanisms. By making use of a range of advanced tools, developers can reveal and confirm resistance pathways and identify the potential vulnerabilities of new therapeutics. Additionally, they can help refine more complex in vivo studies further downstream that use the same cell lines as initial pre-clinical screening.
Application spotlight: complementing patient-derived models
Despite the encouraging clinical efficacy of KRASG12C inhibitors, acquired resistance remains a significant challenge. Secondary KRAS mutations such as G12D, Y96C, R68S, and Q61H can limit the long-term clinical benefits for patients. Therefore, Crown Bioscience has developed a series of KRASG12C inhibitor-resistant tumor models to address this challenge, including:
- Pretreated PDX Models: From non-small cell lung cancer (NSCLC) and colorectal (CRC) patient tumors resistant to KRASG12C inhibitors.
- Drug-Induced Resistant PDX Models: Generated in vivo to mimic resistance in NSCLC.
- Gene-Engineered Resistant Xenograft Models: Using gene-modified pancreatic ductal adenocarcinoma (PDAC) cell lines.
These models can be used to evaluate next-generation KRASG12C inhibitors and combination treatment strategies, providing a valuable in vivo and in vitro evaluation system for drug development.
They are complemented by the large-scale organoid drug screening platform, OrganoidXplore, which can deliver final results within 4-6 weeks.
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Application spotlight: generating multi-faceted models of resistance
48% of all EGFR-mutated NSCLC cases harbor an activating EGFR mutation in exon 19, which confers increased sensitivity to EGFR tyrosine kinase inhibitors (TKIs). Positive clinical responses to EGFR TKIs are observed in these mutated NSCLC cases but after 10-16 months of treatment resistance commonly occurs, resulting in patient relapse.
Crown Bioscience generated drug-resistant models (HCC827) via continuous exposure to EGFR TKIs in vitro. These models have been used to understand mechanisms of resistance and identify strategies to overcome resistance, both in vitro and in vivo. This has led to preclinical studies of new targeted treatments for drug-resistant NSCLC cases and the development of CRISPR-engineered cell line models that mimic resistance to 3rd generation EGFR-inhibitor osimertinib.
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Conclusion
In vitro models, including organoids and cell lines, are powerful tools as they enable detailed characterization and functional genomic profiling in controlled and scalable studies. By integrating these models into wider workflows, researchers can connect them with downstream in vivo and ex vivo platforms to leverage the benefits each system to help streamline drug discovery pipelines and enhance clinical success.
Explore Crown Bioscience’s specialized in vitro resistance modeling services
