Design of personalized optimization of multidrug combinations
It is widely understood that future cancer therapy will be improved by the combination of existing drugs. The main problem with combination of drugs is the occurrence of side effects and the huge number of different possibilities. Therefore, current development of combination therapies is based on the physician's intuition and a trial and error approach.
We have co-developed a new technology of mathematically-assisted navigation in the large search space of drug combinations called Therapeutically Guided Multidrug Optimization, TGMO (Cancers, 2019). This strategy greatly reduces the amount of mechanistic information that needs to be known before the optimization initiation. The TGMO methodology selectively samples a minimal number of experimental data points in order to recreate the cell’s response surface to drug combinations in terms of second-order linear regression models that are used to select synergistic drug combinations (Nature Protocols, 2016). In contrast to other approaches based on pharmacogenetics or high-throughput screening, this methodology is phenotypically-driven and aims to minimize the number of experimental test points as well as cost and time required to make accurate predictions for the optimal combination parameters. Our multiple studies demonstrated success of this strategy in numerous preclinical tumor models (Molecular Oncology 2020; Cancers 2020; Cancers 2019; Angiogenesis, 2015; Scientific Reports 2017).
Drug combinations to boost the activity of immune checkpoint inhibitors
Complex in vitro platforms to mimic tumor microenvironment
We established experimental platforms to mimic human tumors in laboratory settings. It is known that a tumor contains not only cancer cells, but entire microenvironment that is composed of the cellular and the non-cellular compartment. Therefore, we developed 3-dimensional heterotypic co-cultures that contain cancer cells, endothelial cells, fibroblasts, and selected immune cell subsets (Scientific Reports 2019; Cancers 2019, Cancers 2020). Together, these cells produce an extracellular matrix promoting the 3-dimensional structure formation. We use these co-cultures to (i) validate the anti-cancer efficacy of optimized multidrug combinations, (ii) characterize the selective targeting of cancer cells, (iii) monitor the behavior of the cells in response to the applied treatment, (iv) and more.
Identification of the mechanism of action of an optimized drug combination
Our validated Therapeutically Guided Multidrug Optimization (TGMO) platform allowed the identification of a synergistic low-dose four-drug combination composed of two tyrosine kinase inhibitors and two histone deacetylase inhibitors (Cancers 2019). This optimized drug combination (ODC) targets renal cell carcinoma, colorectal carcinoma and melanoma with high efficacy, while showing negligible toxicity towards non-malignant cells. Its activity strongly correlates with the formation of multipolar mitotic spindles and with the inhibition of spindle pole clustering, a specific survival mechanism used by cancer cells with abnormal centrosome numbers or spindle defects to avoid death via multipolar divisions (Cancers 2019; Apoptosis 2021).
Using live cell imaging, high-resolution fluorescence microscopy and pharmacological characterization, and working in close collaboration with cell divisions experts, Prof. Patrick Meraldi's Lab (UNIGE), we aim to identify the full mechanism of action and precise molecular targets of the ODC and validate its efficacy in complex colorectal cancer 3D models, such as patient-derived organoids, highly relevant to human patient pathophysiology.
Acquired drug resistance against targeted- or chemo- therapy
One of the major problems in cancer treatment is the development of drug-acquired resistance. Via chronic treatment with drugs, we have generated treatment-resistant cell lines to study activity of optimized drug combinations (Cancers 2020; Molecules 2020). This was done with sunitinib, tyrosine kinase inhibitor, or a cocktail of chemotherapeutics.
Drug combination delivery through a nanocarrier
One common strategy of treatment of complex diseases, such as AIDS, diabetes, hypertension or bacterial infections is the use of drugs combination. Our lab identified a four-drug combination active in cancer treatment in a synergy-dependent-ratio. However, upon injection, due to different pharmacokinetics of the drugs in combination, the ratio might no longer be respected.
To address this challenge, the optimized four-drug combination will be encapsulated in a nanocarrier, granting a protection from blood proteins with a prolonged blood circulation, reducing side effects while maintaining the drug ratio until it reaches the cancer cell. To this purpose, liposomes are a nanocarrier of choice: they are biocompatible and allow simultaneous encapsulation of hydrophilic and hydrophobic drugs. This project is performed in collaboration with the group of Prof. Gerrit Borchard (ISPSO, UNIGE).