Targeting the hypoxic fraction of tumours using hypoxia activated prodrugs

The presence of a microenvironment within most tumours containing regions of low oxygen tension or hypoxia has profound biological and therapeutic implications. Tumour hypoxia is known to promote the development of an aggressive phenotype, resistance to both chemotherapy and radiotherapy and is strongly associated with poor clinical outcome. Paradoxically, it is recognised as a high priority target and one therapeutic strategies designed to eradicate hypoxic cells in tumours are a group of compounds known collectively as hypoxia activated prodrugs (HAPs) or bioreductive drugs. These drugs are inactive prodrugs that require enzymatic activation (typically by 1 or 2 electron oxidoreductases) to generate cytotoxic species with selectivity for hypoxic cells being determined by (i) the ability of oxygen to either reverse or inhibit the activation process and (ii) the presence of elevated expression of oxidoreductases in tumours. The concepts underpinning HAP development were established over 40 years ago and have been refined over the years to produce a new generation of HAPs that are under preclinical and clinical development. The purpose of this article is to describe current progress in the development of HAPs focusing on the mechanisms of action, preclinical properties and clinical progress of leading examples

Restoring tumour selectivity of the bioreductive prodrug pr-104 by developing an analogue resistant to aerobic metabolism by human aldo-keto reductase 1c3

PR-104 is a phosphate ester pre-prodrug that is converted in vivo to its cognate alcohol, PR-104A, a latent alkylator which forms potent cytotoxins upon bioreduction. Hypoxia selectivity results from one-electron nitro reduction of PR-104A, in which cytochrome P450 oxidoreductase (POR) plays an important role. However, PR-104A also undergoes ‘off-target’ two-electron reduction by human aldo-keto reductase 1C3 (AKR1C3), resulting in activation in oxygenated tissues. AKR1C3 expression in human myeloid progenitor cells probably accounts for the dose-limiting myelotoxicity of PR-104 documented in clinical trials, resulting in human PR-104A plasma exposure levels 3.4- to 9.6-fold lower than can be achieved in murine models. Structure-based design to eliminate AKR1C3 activation thus represents a strategy for restoring the therapeutic window of this class of agent in humans. Here, we identified SN29176, a PR-104A analogue resistant to human AKR1C3 activation. SN29176 retains hypoxia selectivity in vitro with aerobic/hypoxic IC(50) ratios of 9 to 145, remains a substrate for POR and triggers γH2AX induction and cell cycle arrest in a comparable manner to PR-104A. SN35141, the soluble phosphate pre-prodrug of SN29176, exhibited superior hypoxic tumour log cell kill (>4.0) to PR-104 (2.5–3.7) in vivo at doses predicted to be achievable in humans. Orthologues of human AKR1C3 from mouse, rat and dog were incapable of reducing PR-104A, thus identifying an underlying cause for the discrepancy in PR-104 tolerance in pre-clinical models versus humans. In contrast, the macaque AKR1C3 gene orthologue was able to metabolise PR-104A, indicating that this species may be suitable for evaluating the toxicokinetics of PR-104 analogues for clinical development. We confirmed that SN29176 was not a substrate for AKR1C3 orthologues across all four pre-clinical species, demonstrating that this prodrug analogue class is suitable for further development. Based on these findings, a prodrug candidate was subsequently identified for clinical trials

Effects of platinum/taxane based chemotherapy on acute perfusion in human pelvic tumours measured by dynamic MRI

Dynamic contrast enhanced MRI (DCE-MRI) is being used increasingly in clinical trials to demonstrate that vascular disruptive and antiangiogenic agents target tumour microcirculation. Significant reductions in DCE-MRI kinetic parameters are seen within 4–24 and 48 h of treatment with vascular disruptive and antiangiogenic agents, respectively. It is important to know whether cytotoxic agents also cause significant acute reductions in these parameters, for reliable interpretation of results. This study investigated changes in transfer constant (Ktrans) and the initial area under the gadolinium curve (IAUGC) following the first dose of chemotherapy in patients with mostly gynaecological tumours. A reproducibility analysis on 20 patients (using two scans performed on consecutive days) was used to determine the significance of DCE-MRI parameter changes 24 h after chemotherapy in 18 patients. In 11 patients who received platinum alone or with a taxane, there were no significant changes in Ktrans or IAUGC in either group or individual patient analyses. When the remaining seven patients (treated with a variety of agents including platinum and taxanes) were included (n=18), there were also no significant changes in Ktrans. Therefore, if combination therapy does show changes in DCE-MRI parameters then the effects can be attributed to antivascular therapy rather than chemotherapy

Oxygen-Driven Tumour Growth Model : A Pathology-Relevant Mathematical Approach

We acknowledge Lucas Dias Fernandes and Dr Nicolas Rubido from the University of Aberdeen and Dr Neil Evans from the University of Warwick for the broad discussions on the mathematics.Peer reviewedPublisher PD

Novel combination strategies with Aurora Kinase Inhibitors: using pre-clinical data to guide clinical trial design

Anti-mitotic drugs such as paclitaxel are successful chemotherapeutics, but their utility is limited by toxicity and resistance. My research aimed to identify novel therapeutic strategies combining current drugs with aurora kinase inhibitors (AKI) in pre-clinical models and then to design a clinical trial to assess these in patients with bladder cancer. Particular interest was stimulated by the correlation between over-expression of aurora kinases (AKs) with aggressive clinical behaviour in many cancers and the network of interactions which AKs have with other key proteins. Pre-clinical studies suggested that the use of AKIs in combination with other drugs, particularly other anti-mitotic agents, might increase efficacy but this had not been studied in bladder cancer. As over-expression of AK-A can induce resistance to paclitaxel, I hypothesised that an AK-A inhibitor would synergistically enhance the cytotoxicity of paclitaxel. Human bladder cancer cell lines were used in cytotoxicity assays to study a range of novel AKIs as single agents and in combination with paclitaxel. Application of mathematical models identified regions of synergy when AK-A specific inhibitors were combined with low concentrations of paclitaxel in T24, RT112 and UM-UC-3 cell lines, with 80- 100% growth inhibition. The effects of these combinations on non-cancer cell lines and in neutrophil precursor cells demonstrated differential toxicity, with a reduced risk of myelotoxicity compared with higher dose paclitaxel, suggesting they may provide a better therapeutic window. Mechanistic investigations included live cell imaging, which showed a correlation between the time cells spent in mitosis and cell fate, and an assessment of the potential contribution of AK-A expression in sensitivity to the drug combination. Combinations of MLN8237 (an AK-A inhibitor) with paclitaxel and docetaxel were studied in a T24 bladder cancer xenograft model, and these confirmed tolerability with evidence of efficacy in tumour growth inhibition. Published clinical trials have now demonstrated the potential efficacy of AK-A inhibitors in combination with taxanes but at the expense of additive toxicity. My work suggests that using AK-A inhibitors with lower concentrations of paclitaxel could reduce toxicity, highlighting the need to explore a range of combinations. Existing early phase clinical data have emerged from trials using traditional rule-based trial designs, where limited dose ranges were explored. Therefore, an alternative novel Bayesian adaptive design should be considered to fully assess the efficacy of the combination of MLN8237 and paclitaxel.Cancer Research U

Targeting senescence as an anti-cancer therapy

Cellular senescence is a stress response elicited by different molecular insults. Senescence results in cell cycle exit and is characterised by multiple phenotypic changes such as the production of a bioactive secretome. Senescent cells accumulate during ageing and are present in cancerous and fibrotic lesions. Drugs that selectively kill senescent cells (senolytics) have shown great promise for the treatment of age-related diseases. Senescence plays paradoxical roles in cancer. Induction of senescence limits cancer progression and contributes to therapy success, but lingering senescent cells fuel progression, recurrence, and metastasis. In this review, we describe the intricate relation between senescence and cancer. Moreover, we enumerate how current anti-cancer therapies induce senescence in tumour cells and how senolytic agents could be deployed to complement anticancer therapies. "One-two punch" therapies aim to first induce senescence in the tumour followed by senolytic treatment to target newly exposed vulnerabilities in senescent tumour cells. "One-two punch" represents an emerging and promising new strategy in cancer treatment. Future challenges of "one -two punch" approaches include how to best monitor senescence in cancer patients to effectively survey their efficacy

Assessment of modeling strategies for drug response prediction in cell lines and xenografts

Despite significant progress in cancer research, effective cancer treatment is still a challenge. Cancer treatment approaches are shifting from standard cytotoxic chemotherapy regimens towards a precision oncology paradigm, where a choice of treatment is personalized, i.e. based on a tumor’s molecular features. In order to match tumor molecular features with therapeutics we need to identify biomarkers of response and build predictive models. Recent growth of large-scale pharmacogenomics resources which combine drug sensitivity and multi-omics information on a large number of samples provides necessary data for biomarker identification and drug response modelling. However, although many efforts of using this information for drug response prediction have been made, our ability to accurately predict drug response using genetic data remains limited. In this work we used pharmacogenomics data from the largest publicly available studies in order to systematically assess various aspects of the drug response model-building process with the ultimate goal of improving prediction accuracy. We applied several machine learning methods (regularized regression, support vector machines, random forest) for predicting response to a number of drugs. We found that while accuracy of response prediction varies across drugs (in most of the cases R2 values vary between 0.1 and 0.3), different machine learning algorithms applied for the the same drug have similar prediction performance. Experiments with a range of different training sets for the same drug showed that predictive power of a model depends on the type of molecular data, the selected drug response metric, and the size of the training set. It depends less on number of features selected for modelling and on class imbalance in training set. We also implemented and tested two methods for improving consistency for pharmacogenomics data coming from different datasets. We tested our ability to correctly predict response in xenografts and patients using models trained on cell lines. Only in a fraction of the tested cases we managed to get reasonably accurate predictions, particularly in case of response to erlotinib in the NSCLC xenograft cohort, and in cases of responses to erlotinib and docetaxel in the NSCLC and BRCA patient cohorts respectively. This work also includes two applied pharmacogenomics analyses. The first is an analysis of a drug-sensitivity screen performed on a panel of Burkitt cell lines. This combines unsupervised data exploration with supervised modelling. The second is an analysis of drug-sensitivity data for the DKFZ-608 compound and the generation of the corresponding response prediction model. In summary, we applied machine learning techniques to available high-throughput pharmacogenomics data to study the determinants of accurate drug response prediction. Our results can help to draft guidelines for building accurate models for personalized drug response prediction and therefore contribute to advancing of precision oncology

A Chemical Genomics Approach to Drug Reprofiling in Oncology: Antipsychotic Drug Risperidone as a Potential Adenocarcinoma Treatment

Drug reprofiling is emerging as an effective paradigm for discovery of cancer treatments. Herein, an antipsychotic drug is immobilised using the Magic Tag® chemical genomics tool and screened against a T7 bacteriophage displayed library of polypeptides from Drosophila melanogaster, as a whole genome model, to uncover an interaction with a section of 17-β-HSD10, a proposed prostate cancer target. A computational study and enzyme inhibition assay with full length human 17-β-HSD10 identifies risperidone as a drug reprofiling candidate. When formulated with rumenic acid, risperidone slows proliferation of PC3 prostate cancer cells in vitro and retards PC3 prostate cancer tumour growth in vivo in xenografts in mice, presenting an opportunity to reprofile risperidone as a cancer treatment

PDX-Derived Ewing’s Sarcoma Cells Retain High Viability and Disease Phenotype in Alginate Encapsulated Spheroid Cultures

Ewing’s Sarcoma (ES) is the second most frequent malignant bone tumour in children and young adults and currently only untargeted chemotherapeutic approaches and surgery are available as treatment, although clinical trials are on-going for recently developed ES-targeted therapies. To study ES pathobiology and develop novel drugs, established cell lines and patient-derived xenografts (PDX) are the most employed experimental models. Nevertheless, the establishment of ES cell lines is difficult and the extensive use of PDX raises economic/ethical concerns. There is a growing consensus regarding the use of 3D cell culture to recapitulate physiological and pathophysiological features of human tissues, including drug sensitivity. Herein, we implemented a 3D cell culture methodology based on encapsulation of PDX-derived ES cell spheroids in alginate and maintenance in agitation-based culture systems. Under these conditions, ES cells displayed high proliferative and metabolic activity, while retaining the typical EWSR1-FLI1 chromosomal translocation. Importantly, 3D cultures presented reduced mouse PDX cell contamination compared to 2D cultures. Finally, we show that these 3D cultures can be employed in drug sensitivity assays, with results similar to those reported for the PDX of origin. In conclusion, this novel 3D cell culture method involving ES-PDX-derived cells is a suitable model to study ES pathobiology and can assist in the development of novel drugs against this disease, complementing PDX studies.The iNOVA4Health Research Unit (UIDB/04462/2020), cofunded by Fundação para a Ciência e Tecnologia (FCT)/ Ministério da Ciência e do Ensino Superior (MCTES), through national funds, and by FEDER under the PT2020 Partnership Agreement, is acknowledged for financial support. AMC was supported by grants from ISCIII-FEDER (CP13/00189 and CPII18/00009)