How can attrition rates be reduced in cancer drug discovery?

Deep-PK: deep learning for small molecule pharmacokinetic and toxicity prediction

Evaluating pharmacokinetic properties of small molecules is considered a key feature in most drug development and high-throughput screening processes. Generally, pharmacokinetics, which represent the fate of drugs in the human body, are described from four perspectives: absorption, distribution, metabolism and excretion-all of which are closely related to a fifth perspective, toxicity (ADMET). Since obtaining ADMET data from in vitro, in vivo or pre-clinical stages is time consuming and expensive, many efforts have been made to predict ADMET properties via computational approaches. However, the majority of available methods are limited in their ability to provide pharmacokinetics and toxicity for diverse targets, ensure good overall accuracy, and offer ease of use, interpretability and extensibility for further optimizations. Here, we introduce Deep-PK, a deep learning-based pharmacokinetic and toxicity prediction, analysis and optimization platform. We applied graph neural networks and graph-based signatures as a graph-level feature to yield the best predictive performance across 73 endpoints, including 64 ADMET and 9 general properties. With these powerful models, Deep-PK supports molecular optimization and interpretation, aiding users in optimizing and understanding pharmacokinetics and toxicity for given input molecules. The Deep-PK is freely available at https://biosig.lab.uq.edu.au/deeppk/.

Quinoline-based thiazolyl-hydrazones target cancer cells through autophagy inhibition

Heterocyclic pharmacophores such as thiazole and quinoline rings have a significant role in medicinal chemistry. They are considered privileged structures since they constitute several Food and Drug Administration (FDA)-approved drugs for cancer treatment. Herein, we report the synthesis, in silico evaluation of the ADMET profiles, and in vitro investigation of the anticancer activity of a series of novel thiazolyl-hydrazones based on the 8-quinoline (1a-c), 2-quinoline (2a-c), and 8-hydroxy-2-quinolyl moiety (3a-c). The panel of several human cancer cell lines and the nontumorigenic human embryonic kidney cell line HEK-293 were used to evaluate the compound-mediated in vitro anticancer activities, leading to [2-(2-(quinolyl-8-ol-2-ylmethylene)hydrazinyl)]-4-(4-methoxyphenyl)-1,3-thiazole (3c) as the most promising compound. The study revealed that 3c blocks the cell-cycle progression of a human colon cancer cell line (HCT-116) in the S phase and induces DNA double-strand breaks. Also, our findings demonstrate that 3c accumulates in lysosomes, ultimately leading to the cell death of the hepatocellular carcinoma cell line (Hep-G2) and HCT-116 cells, by the mechanism of autophagy inhibition.

AI-Driven Enhancements in Drug Screening and Optimization

The greatest challenge in drug discovery remains the high rate of attrition across the different phases of the process, which cost the industry billions of dollars every year. While all phases remain crucial to ensure pharmaceutical-level safety, quality, and efficacy of the end product, streamlining these efforts toward compounds with success potential is pivotal for a more efficient and cost-effective process. The use of artificial intelligence (AI) within the pharmaceutical industry aims at just this, and has applications in preclinical screening for biological activity, optimization of pharmacokinetic properties for improved drug formulation, early toxicity prediction which reduces attrition, and pre-emptively screening for genetic changes in the biological target to improve therapeutic longevity. Here, we present a series of in silico tools that address these applications in small molecule development and describe how they can be embedded within the current pharmaceutical development pipeline.

A human mesenchymal spheroid prototype to replace moderate severity animal procedures in leukaemia drug testing

Patient derived xenograft (PDX) models are regarded as gold standard preclinical models in leukaemia research, especially in testing new drug combinations where typically 45-50 mice are used per assay. 9000 animal experiments are performed annually in the UK in leukaemia research with these expensive procedures being classed as moderate severity, meaning they cause significant pain, suffering and visible distress to animal's state. Furthermore, not all clinical leukaemia samples engraft and when they do data turnaround time can be between 6-12 months. Heavy dependence on animal models is because clinical leukaemia samples do not proliferate in vitro. Alternative cell line models though popular for drug testing are not biomimetic - they are not dependent on the microenvironment for survival, growth and treatment response and being derived from relapse samples they do not capture the molecular complexity observed at disease presentation. Here we have developed an in vitro platform to rapidly establish co-cultures of patient-derived leukaemia cells with 3D bone marrow mesenchyme spheroids, BM-MSC-spheroids.  We optimise protocols for developing MSC-spheroid leukaemia co-culture using clinical samples and deliver drug response data within a week. Using three patient samples representing distinct cytogenetics we show that patient-derived-leukaemia cells show enhanced proliferation when co-cultured with MSC-spheroids. In addition, MSC-spheroids provided improved protection against treatment. This makes our spheroids suitable to model treatment resistance - a major hurdle in current day cancer management Given this 3Rs approach is 12 months faster (in delivering clinical data), is a human cell-based biomimetic model and uses 45-50 fewer animals/drug-response assay the anticipated target end-users would include academia and pharmaceutical industry. This animal replacement prototype would facilitate clinically translatable research to be performed with greater ethical, social and financial sustainability.

Next generation organoid engineering to replace animals in cancer drug testing

Cancer therapies have several clinical challenges associated with them, namely treatment toxicity, treatment resistance and relapse. Due to factors ranging from patient profiles to the tumour microenvironment (TME), there are several hurdles to overcome in developing effective treatments that have low toxicity that can mitigate emergence of resistance and occurrence of relapse. De novo cancer development has the highest drug attrition rates with only 1 in 10,000 preclinical candidates reaching the market. To alleviate this high attrition rate, more mimetic and sustainable preclinical models that can capture the disease biology as in the patient, are required. Organoids and next generation 3D tissue engineering is an emerging area that aims to address this problem. Advancement of three-dimensional (3D) in vitro cultures into complex organoid models incorporating multiple cell types alongside acellular aspects of tissue microenvironments can provide a system for therapeutic testing. Development of microfluidic technologies have furthermore increased the biomimetic nature of these models. Additionally, 3D bio-printing facilitates generation of tractable ex vivo models in a controlled, scalable and reproducible manner. In this review we highlight some of the traditional preclinical models used in cancer drug testing and debate how next generation organoids are being used to replace not only animal models, but also some of the more elementary in vitro approaches, such as cell lines. Examples of applications of the various models will be appraised alongside the future challenges that still need to be overcome.

Validation of a 3D perfused cell culture platform as a tool for humanised preclinical drug testing in breast cancer using established cell lines and patient-derived tissues

3D cell culture models of cancer are currently being developed to recapitulate in vivo physiological conditions and to assess therapeutic responses. However, most models failed to incorporate the biochemical and biophysical stimuli from fluid flow. In this study, a three-dimensional scaffold, SeedEZ was applied within the PerfusionPal perfused culture system to investigate how perfusion, and blood-like oxygen delivery influenced breast cancer cell growth and their responses to a commonly used breast cancer drug tamoxifen. Our results showed that breast cancer cells could be maintained over 3 weeks in PerfusionPal with increased cell viability compared to static 3D culture in fully humanised conditions. This platform also supported examining the effect of tamoxifen on breast cancer cell lines and in primary patient-derived breast cancer samples. Future work is warranted to further the adaption for fully humanised assessment of drug effectiveness in a patient personalized approach with the aim to reduce the burden of animal use in cancer research and increase the degree of human pre-clinical data translation to clinic.

In Silico Modeling Demonstrates that User Variability During Tumor Measurement Can Affect In Vivo Therapeutic Efficacy Outcomes

User measurement bias during subcutaneous tumor measurement is a source of variation in preclinical in vivo studies. We investigated whether this user variability could impact efficacy study outcomes, in the form of the false negative result rate when comparing treated and control groups. Two tumor measurement methods were compared; calipers which rely on manual measurement, and an automatic 3D and thermal imaging device. Tumor growth curve data were used to create an in silico efficacy study with control and treated groups. Before applying user variability, treatment group tumor volumes were statistically different to the control group. Utilizing data collected from 15 different users across 9 in vivo studies, user measurement variability was computed for both methods and simulation was used to investigate its impact on the in silico study outcome. User variability produced a false negative result in 0.7% to 18.5% of simulated studies when using calipers, depending on treatment efficacy. When using an imaging device with lower user variability this was reduced to 0.0% to 2.6%, demonstrating that user variability impacts study outcomes and the ability to detect treatment effect. Reducing variability in efficacy studies can increase confidence in efficacy study outcomes without altering group sizes. By using a measurement device with lower user variability, the chance of missing a therapeutic effect can be reduced and time and resources spent pursuing false results could be saved. This improvement in data quality is of particular interest in discovery and dosing studies, where being able to detect small differences between groups is crucial.

Data-driven analysis and druggability assessment methods to accelerate the identification of novel cancer targets

Over the past few decades, drug discovery has greatly improved the outcomes for patients, but several challenges continue to hinder the rapid development of novel drugs. Addressing unmet clinical needs requires the pursuit of drug targets that have a higher likelihood to lead to the development of successful drugs. Here we describe a bioinformatic approach for identifying novel cancer drug targets by performing statistical analysis to ascertain quantitative changes in expression levels between protein-coding genes, as well as co-expression networks to classify these genes into groups. Subsequently, we provide an overview of druggability assessment methodologies to prioritize and select the best targets to pursue.

toxCSM: comprehensive prediction of small molecule toxicity profiles

Drug discovery is a lengthy, costly and high-risk endeavour that is further convoluted by high attrition rates in later development stages. Toxicity has been one of the main causes of failure during clinical trials, increasing drug development time and costs. To facilitate early identification and optimisation of toxicity profiles, several computational tools emerged aiming at improving success rates by timely pre-screening drug candidates. Despite these efforts, there is an increasing demand for platforms capable of assessing both environmental as well as human-based toxicity properties at large scale. Here, we present toxCSM, a comprehensive computational platform for the study and optimisation of toxicity profiles of small molecules. toxCSM leverages on the well-established concepts of graph-based signatures, molecular descriptors and similarity scores to develop 36 models for predicting a range of toxicity properties, which can assist in developing safer drugs and agrochemicals. toxCSM achieved an Area Under the Receiver Operating Characteristic (ROC) Curve (AUC) of up to 0.99 and Pearson's correlation coefficients of up to 0.94 on 10-fold cross-validation, with comparable performance on blind test sets, outperforming all alternative methods. toxCSM is freely available as a user-friendly web server and API at http://biosig.lab.uq.edu.au/toxcsm.

Drosophila melanogaster: A platform for anticancer drug discovery and personalized therapies

Cancer is a complex disease whereby multiple genetic aberrations, epigenetic modifications, metabolic reprogramming, and the microenvironment contribute to the development of a tumor. In the traditional anticancer drug discovery pipeline, drug candidates are usually screened in vitro using two-dimensional or three-dimensional cell culture. However, these methods fail to accurately mimic the human disease state. This has led to the poor success rate of anticancer drugs in the preclinical stages since many drugs are abandoned due to inefficacy or toxicity when transitioned to whole-organism models. The common fruit fly, Drosophila melanogaster, has emerged as a beneficial system for modeling human cancers. Decades of fundamental research have shown the evolutionary conservation of key genes and signaling pathways between flies and humans. Moreover, Drosophila has a lower genetic redundancy in comparison to mammals. These factors, in addition to the advancement of genetic toolkits for manipulating gene expression, allow for the generation of complex Drosophila genotypes and phenotypes. Numerous studies have successfully created Drosophila models for colorectal, lung, thyroid, and brain cancers. These models were utilized in the high-throughput screening of FDA-approved drugs which led to the identification of several compounds capable of reducing proliferation and rescuing phenotypes. More noteworthy, Drosophila has also unlocked the potential for personalized therapies. Drosophila ‘avatars’ presenting the same mutations as a patient are used to screen multiple therapeutic agents targeting multiple pathways to find the most appropriate combination of drugs. The outcomes of these studies have translated to significant responses in patients with adenoid cystic carcinoma and metastatic colorectal cancers. Despite not being widely utilized, the concept of in vivo screening of drugs in Drosophila is making significant contributions to the current drug discovery pipeline. In this review, we discuss the application of Drosophila as a platform in anticancer drug discovery; with special focus on the cancer models that have been generated, drug libraries that have been screened and the status of personalized therapies. In addition, we elaborate on the biological and technical limitations of this system.

Progressing Towards a Human-Centric Approach in Cancer Research

Despite the advancement in research methodologies and technologies for cancer research, there is a high rate of anti-cancer drug attrition. In this review, we discuss different conventional and modern approaches in cancer research and how human-centric models can improve on the voids conferred by more traditional animal-centric models, thereby offering a more reliable platform for drug discovery. Advanced three-dimensional cell culture methodologies, along with in silico computational analysis form the core of human-centric cancer research. This can provide a holistic understanding of the research problems and help design specific and accurate experiments that could lead to the development of better cancer therapeutics. Here, we propose a new human-centric research roadmap that promises to provide a better platform for cancer research and drug discovery.

Utilizing Three-Dimensional Culture Methods to Improve High-Throughput Drug Screening in Anaplastic Thyroid Carcinoma

Anaplastic thyroid carcinoma (ATC) is the most aggressive endocrine neoplasm, with a median survival of just four to six months post-diagnosis. Even with surgical and chemotherapeutic interventions, the five-year survival rate is less than 5%. Although combination dabrafenib/trametinib therapy was recently approved for treatment of the ~25% of ATCs harboring BRAFV600E mutations, there are no approved, effective treatments for BRAF-wildtype disease. Herein, we perform a screen of 1525 drugs and evaluate therapeutic candidates using monolayer cell lines and four corresponding spheroid models of anaplastic thyroid carcinoma. We utilize three-dimensional culture methods, as they have been shown to more accurately recapitulate tumor responses in vivo. These three-dimensional cultures include four distinct ATC spheroid lines representing unique morphology and mutational drivers to provide drug prioritization that will be more readily translatable to the clinic. Using this screen, we identify three exceptionally potent compounds (bortezomib, cabazitaxel, and YM155) that have established safety profiles and could potentially be moved into clinical trial for the treatment of anaplastic thyroid carcinoma, a disease with few treatment options.

Harnessing the Heterogeneity of Prostate Cancer for Target Discovery Using Patient-Derived Explants

Simple Summary

There is a widespread push toward more biologically relevant pre-clinical models of prostate cancer that can improve the discovery and translation of new drugs and biomarkers for this disease. Patient-derived explant culture is an innovative pre-clinical model that utilizes surgical prostate cancer specimens in a way that retains the architecture, microenvironment and heterogeneity of prostate tumors—factors that critically influence cell behavior and response to therapy. With increasing tissue complexity comes increasing complexity of analysis. The aim of this study was to provide critical information for the successful application and analysis of the patient-derived prostate cancer explant model.

Abstract

Prostate cancer is a complex and heterogeneous disease, but a small number of cell lines have dominated basic prostate cancer research, representing a major obstacle in the field of drug and biomarker discovery. A growing lack of confidence in cell lines has seen a shift toward more sophisticated pre-clinical cancer models that incorporate patient-derived tumors as xenografts or explants, to more accurately reflect clinical disease. Not only do these models retain critical features of the original tumor, and account for the molecular diversity and cellular heterogeneity of prostate cancer, but they provide a unique opportunity to conduct research in matched tumor samples. The challenge that accompanies these complex tissue models is increased complexity of analysis. With over 10 years of experience working with patient-derived explants (PDEs) of prostate cancer, this study provides guidance on the PDE method, its limitations, and considerations for addressing the heterogeneity of prostate cancer PDEs that are based on statistical modeling. Using inhibitors of the molecular chaperone heat shock protein 90 (Hsp90) as an example of a drug that induces robust proliferative response, we demonstrate how multi-omics analysis in prostate cancer PDEs is both feasible and essential for identification of key biological pathways, with significant potential for novel drug target and biomarker discovery.

Quantification of long-term doxorubicin response dynamics in breast cancer cell lines to direct treatment schedules

While acquired chemoresistance is recognized as a key challenge to treating many types of cancer, the dynamics with which drug sensitivity changes after exposure are poorly characterized. Most chemotherapeutic regimens call for repeated dosing at regular intervals, and if drug sensitivity changes on a similar time scale then the treatment interval could be optimized to improve treatment performance. Theoretical work suggests that such optimal schedules exist, but experimental confirmation has been obstructed by the difficulty of deconvolving the simultaneous processes of death, adaptation, and regrowth taking place in cancer cell populations. Here we present a method of optimizing drug schedules in vitro through iterative application of experimentally calibrated models, and demonstrate its ability to characterize dynamic changes in sensitivity to the chemotherapeutic doxorubicin in three breast cancer cell lines subjected to treatment schedules varying in concentration, interval between pulse treatments, and number of sequential pulse treatments. Cell populations are monitored longitudinally through automated imaging for 600–800 hours, and this data is used to calibrate a family of cancer growth models, each consisting of a system of ordinary differential equations, derived from the bi-exponential model which characterizes resistant and sensitive subpopulations. We identify a model incorporating both a period of growth arrest in surviving cells and a delay in the death of chemosensitive cells which outperforms the original bi-exponential growth model in Akaike Information Criterion based model selection, and use the calibrated model to quantify the performance of each drug schedule. We find that the inter-treatment interval is a key variable in determining the performance of sequential dosing schedules and identify an optimal retreatment time for each cell line which extends regrowth time by 40%-239%, demonstrating that the time scale of changes in chemosensitivity following doxorubicin exposure allows optimization of drug scheduling by varying this inter-treatment interval.

Acquired chemoresistance is a common cause of treatment failure in cancer. The scheduling of a multi-dose course of chemotherapeutic treatment may influence the dynamics of acquired chemoresistance, and drug schedule optimization may increase the duration of effectiveness of a particular chemotherapeutic agent for a particular patient. Here we present a method for experimentally optimizing an in vitro drug schedule through iterative rounds of experimentation and computational analysis, and demonstrate the method’s ability to improve the performance of doxorubicin treatment in three breast carcinoma cell lines. Specifically, we find that the interval between drug exposures can be optimized while holding drug concentration and number of treatments constant, suggesting that this may be a key variable to explore in future drug schedule optimization efforts. We further use this method’s model calibration and selection process to extract information about the underlying biology of the doxorubicin response, and find that the incorporation of delays on both cell death and regrowth are necessary for accurate parameterization of cell growth data.

Drug repurposing for cancer therapy in the era of precision medicine

Drug repurposing refers to the identification of clinically approved drugs, with the known safety profiles and defined pharmacokinetic properties, to new indications. Despite the advances in oncology research, cancers are still associated with the most unmet medical needs. Drug repurposing has emerged as a useful approach for the search for effective and durable cancer treatment. It may also represent a promising strategy to facilitate precision cancer treatment and to overcome drug resistance. The repurposing of non-cancer drugs for precision oncology effectively extends the inventory of actionable molecular targets and thus increases the number of patients who may benefit from precision cancer treatment. In cancer types where genetic heterogeneity is so high that it is not feasible to identify strong repurposed drug candidates for standard treatment, the precision oncology approach offers individual patients access to novel treatment options. For repurposed candidates with low potency, a combination of multiple repurposed drugs may produce a synergistic therapeutic effect. Precautions should be taken when combining repurposed drugs with anticancer agents to avoid detrimental drug-drug interactions and unwanted side effects. New multifactorial data analysis and artificial intelligence methods are needed to untangle the complex association of molecular signatures influencing specific cancer subtypes to facilitate drug repurposing in precision oncology.

Companion Animal Model in Translational Oncology; Feline Oral Squamous Cell Carcinoma and Canine Oral Melanoma

Simple Summary

Laboratory rodents are the most common animal models used in preclinical cancer research. Companion animals with naturally occurring cancers are an under-utilized natural model for the development of new anti-cancer drugs. Dogs and cats develop several types of cancers that resemble those arising in humans with similar clinical and histopathological features and often with similar molecular and genetic backgrounds. Exposure to environmental carcinogens, including air, food and water are also common between people and their pets. Dogs and cats are a unique model that could be integrated between the preclinical laboratory animal model and human clinical trials.

Abstract

Companion animals with naturally occurring cancers can provide an advantageous model for cancer research and in particular anticancer drug development. Compared to commonly utilized mouse models, companion animals, specifically dogs and cats, share a closer phylogenetical distance, body size, and genome organization. Most importantly, pets develop spontaneous, rather than artificially induced, cancers. The incidence of cancer in people and companion animals is quite similar and cancer is the leading cause of death in dogs over 10 years of age. Many cancer types in dogs and cats have similar pathological, molecular, and clinical features to their human counterparts. Drug toxicity and response to anti-cancer treatment in dogs and cats are also similar to those in people. Companion animals share their lives with their owners, including the environmental and socioeconomic cancer-risk factors. In contrast to humans, pets have a shorter life span and cancer progression is often more rapid. Clinical trials in companion animals are cheaper and less time consuming compared to human trials. Dogs and cats with naturally occurring cancers are an ideal and unique model for human cancer research. Model selection for the specific type of cancer is of pivotal importance. Although companion animal models for translational research have been reviewed previously, this review will try to summarize the most important advantages and disadvantages of this model. Feline oral squamous cell carcinoma as a model for head and neck squamous cell carcinoma and canine oral melanoma as a model for mucosal melanoma and immunotherapy in people will be discussed as examples.

Preclinical Solid Tumor Models to Study Novel Therapeutics in Brain Metastases

Metastases are the most common malignancy of the adult central nervous system and are becoming an increasingly troubling problem in oncology largely due to the lack of successful therapeutic options. The limited selection of treatments is a result of the currently poor understanding of the biological mechanisms of metastatic development, which in turn is difficult to achieve because of limited preclinical models that can accurately represent the clinical progression of metastasis. Described in this article are in vitro and in vivo model systems that are used to enhance the understanding of metastasis and to identify new therapies for the treatment of brain metastasis. © 2021 Wiley Periodicals LLC.

Application of Human Induced Pluripotent Stem Cell-Derived Cellular and Organoid Models for COVID-19 Research

The outbreak of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and its rapid international spread has caused the coronavirus disease 2019 (COVID-19) pandemics, which is a global public health crisis. Thus, there is an urgent need to establish biological models to study the pathology of SARS-CoV-2 infection, which not only involves respiratory failure, but also includes dysregulation of other organs and systems, including the brain, heart, liver, intestines, pancreas, kidneys, eyes, and so on. Cellular and organoid models derived from human induced pluripotent stem cells (iPSCs) are ideal tools for in vitro simulation of viral life cycles and drug screening to prevent the reemergence of coronavirus. These iPSC-derived models could recapitulate the functions and physiology of various human cell types and assemble the complex microenvironments similar with those in the human organs; therefore, they can improve the study efficiency of viral infection mechanisms, mimic the natural host-virus interaction, and be suited for long-term experiments. In this review, we focus on the application of in vitro iPSC-derived cellular and organoid models in COVID-19 studies.

3D Cancer Models: Depicting Cellular Crosstalk within the Tumour Microenvironment

The tumour microenvironment plays a critical role in tumour progression and drug resistance processes. Non-malignant cell players, such as fibroblasts, endothelial cells, immune cells and others, interact with each other and with the tumour cells, shaping the disease. Though the role of each cell type and cell communication mechanisms have been progressively studied, the complexity of this cellular network and its role in disease mechanism and therapeutic response are still being unveiled. Animal models have been mainly used, as they can represent systemic interactions and conditions, though they face recognized limitations in translational potential due to interspecies differences. In vitro 3D cancer models can surpass these limitations, by incorporating human cells, including patient-derived ones, and allowing a range of experimental designs with precise control of each tumour microenvironment element. We summarize the role of each tumour microenvironment component and review studies proposing 3D co-culture strategies of tumour cells and non-malignant cell components. Moreover, we discuss the potential of these modelling approaches to uncover potential therapeutic targets in the tumour microenvironment and assess therapeutic efficacy, current bottlenecks and perspectives.

Cell Models for Chromosome 20q11.21 Amplification and Drug Sensitivities in Colorectal Cancer

Background and objectives: The chromosome locus 20q11.21 is a commonly amplified locus in colorectal cancer, with a prevalence of 8% to 9%. Several candidate cancer-associated genes are transcribed from the locus. The therapeutic implications of the amplification in colorectal cancer remain unclear. Materials and Methods: Preclinical cell line models of colorectal cancer included in the Cancer Cell Line Encyclopedia (CCLE) collection were examined for the presence of amplifications in 20q11.21 genes. Correlations of the presence of 20q11.21 amplifications with gene essentialities and drug sensitivities were surveyed on salient databases for determination of therapeutic leads. Results: A significant subset of colorectal cancer cell lines in the CCLE (12 of 63 cell lines, 19%) bear amplifications of genes located at 20q11.21. Cancer-associated genes of the locus include ASXL1, DNMT3B, BCL2L1, TPX2, KIF3B and POFUT1. These genes are all amplified in the 12 cell lines, but they are variably over-expressed at the mRNA level, compared to non-amplified lines. 20q11.21 amplified cell lines are sensitive to various tyrosine kinase inhibitors and are resistant to chemotherapy drugs targeting the mitotic apparatus and microtubules. CRISPR and RNAi dependencies screening revealed, besides the β-catenin and KRAS genes, a few recurrent gene dependencies in more than one cell line, including YAP1 and JUP. Conclusions: Cell line models of colorectal cancer with 20q11.21 gene amplifications display dependencies on the presence of specific genes and resistance or sensitivity to specific drugs and drug categories. Observations from in vitro models may form the basis for clinical drug development in this subtype of colorectal cancer. Genetic lesions conferring synthetic lethality to certain drugs or categories of drugs could be discovered with this approach.

3D Tumor Models for Breast Cancer: Whither We Are and What We Need

Three-dimensional (3D) models have led to a paradigm shift in disease modeling in vitro, particularly for cancer. The past decade has seen a phenomenal increase in the development of 3D models for various types of cancers with a focus on studying stemness, invasive behavior, angiogenesis, and chemoresistance of cancer cells, as well as contributions of its stroma, which has expanded our understanding of these processes. Cancer biology is moving into exploring the emerging hallmarks of cancer, such as inflammation, immune evasion, and reprogramming of energy metabolism. Studies into these emerging concepts have provided novel targets and treatment options such as antitumor immunotherapy. However, 3D models that can investigate the emerging hallmarks are few and underexplored. As commonly used immunocompromised mice and syngenic mice cannot accurately mimic human immunology, stromal interactions, and metabolism and require the use of prohibitively expensive humanized mice, there is tremendous scope to develop authentic 3D tumor models in these areas. Taking the specific case of breast cancer, we discuss the currently available 3D models, their applications to mimic signaling in cancer, tumor-stroma interactions, drug responses, and assessment of drug delivery systems and therapies. We discuss the lacunae in the development of 3D tumor models for the emerging hallmarks of cancer, for lesser-explored forms of breast cancer, and provide insights to develop such models. We discuss how the next generation of 3D models can provide a better mimic of human cancer modeling compared to xenograft models and the scope toward preclinical models and precision medicine.

Aldehyde Dehydrogenase, a Therapeutic Target in Chordoma: Analysis in 3D Cellular Models

Chordomas are rare, slow-growing tumors of the axial skeleton. These tumors are locally aggressive and refractory to conventional therapies. Radical surgery and radiation remain the first-line treatments. Despite these aggressive treatments, chordomas often recur and second-line treatment options are limited. The mechanisms underlying chordoma radioresistance remain unknown, although several radioresistant cancer cells have been shown to respond favorably to aldehyde dehydrogenase (ALDH) inhibition. The study of chordoma has been delayed by small patient cohorts and few available models due to the scarcity of these tumors. We thus created cellular 3D models of chordoma by using low-adherence culture systems. Then, we evaluated their radiosensitivity using colony-forming and spheroid size assays. Finally, we determined whether pharmacologically inhibiting ALDH increased their radiosensitivity. We found that 3D cellular models of chordoma (derived from primary, relapse, and metastatic tumors) reproduce the histological and gene expression features of the disease. The metastatic, relapse, and primary spheroids displayed high, medium, and low radioresistance, respectively. Moreover, inhibiting ALDH decreased the radioresistance in all three models.

The application of isatin-based multicomponent-reactions in the quest for new bioactive and druglike molecules

Oxindole derivatives are known for their great interest in the field of Medicinal Chemistry, as they display vast biological activities. Recent efforts concerning the preparation of oxindole derivatives using isatin-based multicomponent reactions (MCRs) constitute a great advance in generating druglike libraries fast and with wide scaffold diversity. In this review, we address those recent developments, exploring the synthetic pathways and biological activities described for these compounds, namely antitumor, antibacterial, antifungal, antiparasitic, antiviral, antioxidant, anti-inflammatory and central nervous system (CNS) pathologies. To add new depth to this work, we used a well-established web-based free tool (SwissADME) to evaluate the most promising scaffolds in what concerns their druglike properties, namely by evaluating their compliance with some of the most valuable rules applied by medicinal chemists in both academia and industrial settings (Lipinski, Ghose, Veber, Egan, Muegge). The aim of this review is to endorse isatin-based MCRs as a valuable synthetic approach to attain new hit compounds bearing the oxindole privileged structure, while critically exploring these scaffolds' druglike properties.

Cucurbitacin mediated regulation of deregulated oncogenic signaling cascades and non-coding RNAs in different cancers: Spotlight on JAK/STAT, Wnt/β-catenin, mTOR, TRAIL-mediated pathways

Research over decades has enabled us in developing a better understanding of the multifaceted and heterogeneous nature of cancer. High-throughput technologies have helped the researchers in unraveling of the underlying mechanisms which centrally regulate cancer onset, metastasis and drug resistance. Our rapidly expanding knowledge about signal transduction cascade has added another layer of complexity to already complicated nature of cancer. Deregulation of cell signaling pathways played a linchpin role in carcinogenesis and metastasis. Cucurbitacins have gained tremendous attention because of their remarkable pharmacological properties and considerable ability to mechanistically modulate myriad of cell signaling pathways in different cancers. In this review, we have attempted to provide a mechanistic and comprehensive analysis of regulation of oncogenic pathways by cucurbitacins in different cancers. We have partitioned this review into separate sections for exclusive analysis of each signaling pathway and critical assessment of the knowledge gaps. In this review, we will summarize most recent and landmark developments related to regulation of Wnt/β-catenin, JAK/STAT, mTOR, VEGFR, EGFR and Hippo pathway by cucurbitacins. Moreover, we will also address how cucurbitacins regulate DNA damage repair pathway and TRAIL-driven signaling in various cancers. However, there are still outstanding questions related to regulation of SHH/GLI, TGF/SMAD and Notch-driven pathway by cucurbitacins in different cancers. Future studies must converge on the analysis of full-fledge potential of cucurbitacins by in-depth analysis of these pathways and how these pathways can be therapeutically targeted by cucurbitacins.

Antiviral activity and safety of remdesivir against SARS-CoV-2 infection in human pluripotent stem cell-derived cardiomyocytes

Coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), is considered as the most significant global public health crisis of the century. Several drug candidates have been suggested as potential therapeutic options for COVID-19, including remdesivir, currently the only authorized drug for use under an Emergency Use Authorization. However, there is only limited information regarding the safety profiles of the proposed drugs, in particular drug-induced cardiotoxicity. Here, we evaluated the antiviral activity and cardiotoxicity of remdesivir using cardiomyocytes-derived from human pluripotent stem cells (hPSC-CMs) as an alternative source of human primary cardiomyocytes (CMs). In this study, remdesivir exhibited up to 60-fold higher antiviral activity in hPSC-CMs compared to Vero E6 cells; however, it also induced moderate cardiotoxicity in these cells. To gain further insight into the drug-induced arrhythmogenic risk, we assessed QT interval prolongation and automaticity of remdesivir-treated hPSC-CMs using a multielectrode array (MEA). As a result, the data indicated a potential risk of QT prolongation when remdesivir is used at concentrations higher than the estimated peak plasma concentration. Therefore, we conclude that close monitoring of the electrocardiographic/QT interval should be advised in SARS-CoV-2-infected patients under remdesivir medication, in particular individuals with pre-existing heart conditions.

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Remdesivir potently inhibits SARS-CoV-2 infection in hPSC-CMs with EC₅₀ at high nanomolar concentrations.

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Remdesivir-induced arrhythmogenic risk in hPSC-CMs was evaluated by multi-electrode array-based assay.

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Remdesivir exhibits potential cardiotoxicity in hPSC-CMs with CC₅₀ values close to the estimated peak plasma concentration.

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Close monitoring of the electrocardiographic/QT interval should be advised in COVID-19 patients under remdesivir medication.

Physiologically-Based Pharmacokinetic/Pharmacodynamic Model of MBQ-167 to Predict Tumor Growth Inhibition in Mice

MBQ-167 is a dual inhibitor of the Rho GTPases Rac and Cdc42 that has shown promising results as an anti-cancer therapeutic at the preclinical stage. This drug has been tested in vitro and in vivo in metastatic breast cancer mouse models. The aim of this study is to develop a physiologically based pharmacokinetic/pharmacodynamic (PBPK-PD) model of MBQ-167 to predict tumor growth inhibition following intraperitoneal (IP) administration in mice bearing Triple Negative and HER2+ mammary tumors. PBPK and Simeoni tumor growth inhibition (TGI) models were developed using the Simcyp V19 Animal Simulator. Our developed PBPK framework adequately describes the time course of MBQ-167 in each of the mouse tissues (e.g., lungs, heart, liver, kidneys, spleen, plasma) and tumor, since the predicted results were consistent with the experimental data. The developed PBPK-PD model successfully predicts tumor shrinkage in HER2+ and triple-negative breast tumors after the intraperitoneal administration of 1 and 10 mg/kg body weight (BW) dose level of MBQ-167 three times a week. The findings from this study suggest that MBQ-167 has a higher net effect and potency inhibiting Triple Negative mammary tumor growth compared to HER2+ and that liver metabolism is the major route of elimination of this drug.

COVID-19: An overview of the current pharmacological interventions, vaccines, and clinical trials

COVID-19, the greatest public health emergency of the 21st century, has affected 215 countries and territories around the world resulting in 15,151,738 confirmed cases and 621,121 deaths. The outbreak has continued at breakneck pace despite stringent public health measures, ravaging the global economy and causing profound human casualties. Vaccination is currently the best bet for the prevention of COVID-19. Still, in its absence, there has been considerable interest in repurposing existing therapeutic agents to reduce the severity of the illness and ease the burden on the already strained healthcare systems. This review outlines the current evidence regarding proposed treatments- experimental or repurposed, for COVID-19, and gives an insight into the clinical trial landscape for drugs as well as vaccines.

Metastasis-Specific Gene Expression in Autochthonous and Allograft Mouse Mammary Tumor Models: Stratification and Identification of Targetable Signatures

Breast cancer metastasis is a leading cause of cancer-related death of women in the United States. A hurdle in advancing metastasis-targeted intervention is the phenotypic heterogeneity between primary and secondary lesions. To identify metastasis-specific gene expression profiles we performed RNA-sequencing of breast cancer mouse models; analyzing metastases from models of various drivers and routes. We contrasted the models and identified common, targetable signatures. Allograft models exhibited more mesenchymal-like gene expression than genetically engineered mouse models (GEMM), and primary culturing of GEMM-derived metastatic tissue induced mesenchymal-like gene expression. In addition, metastasis-specific transcriptomes differed between tail vein and orthotopic injection of the same cell line. Gene expression common to models of spontaneous metastasis included sildenafil response and nicotine degradation pathways. Strikingly, in vivo sildenafil treatment significantly reduced metastasis by 54%, while nicotine significantly increased metastasis by 46%. These data suggest that (i) actionable metastasis-specific pathways can be readily identified, (ii) already available drugs may have great potential to alleviate metastatic incidence, and (iii) metastasis may be influenced greatly by lifestyle choices such as the choice to consume nicotine products. In summary, while mouse models of breast cancer metastasis vary in ways that must not be ignored, there are shared features that can be identified and potentially targeted therapeutically. IMPLICATIONS: The data we present here exposes critical variances between preclinical models of metastatic breast cancer and identifies targetable pathways integral to metastatic spread. VISUAL OVERVIEW: http://mcr.aacrjournals.org/content/molcanres/18/9/1278/F1.large.jpg.

Porous Polymeric Microspheres With Controllable Pore Diameters for Tissue Engineered Lung Tumor Model Development

Complex cell cultures are more representative of in vivo conditions than conventionally used monolayer cultures, and are hence being investigated for predictive screening of therapeutic agents. Poly lactide co-glycolide (PLGA) polymer is frequently used in the development of porous substrates for complex cell culture. Substrates or scaffolds with highly interconnected, micrometric pores have been shown to positively impact tissue model formation by enhancing cell attachment and infiltration. We report a novel alginate microsphere (AMS)-based controlled pore formation method for the development of porous, biodegradable PLGA microspheres (PPMS), for tissue engineered lung tumor model development. The AMS porogen, non-porous PLGA microspheres (PLGAMS) and PPMS had spherical morphology (mean diameters: 10.3 ± 4, 79 ± 21.8, and 103 ± 30 μm, respectively). The PPMS had relatively uniform pores and a porosity of 45.5%. Degradation studies show that PPMS effectively maintained their structural integrity with time whereas PLGAMS showed shrunken morphology. The optimized cell seeding density on PPMS was 25 × 103 cells/mg of particles/well. Collagen coating on PPMS significantly enhanced the attachment and proliferation of co-cultures of A549 lung adenocarcinoma and MRC-5 lung fibroblast cells. Preliminary proof-of-concept drug screening studies using mono- and combination anti-cancer therapies demonstrated that the tissue-engineered lung tumor model had a significantly higher resistance to the tested drugs than the monolayer co-cultures. These studies indicate that the PPMS with controllable pore diameters may be a suitable platform for the development of complex tumor cultures for early in vitro drug screening applications.

Identifying the Responses from the Estrogen Receptor-Expressed MCF7 Cells Treated in Anticancer Drugs of Different Modes of Action Using Live-Cell FTIR Spectroscopy

Recently, we have shown that changes in Fourier transform infrared (FTIR) spectra of living MDA-MB-231 cells (a triple negative cell line) upon exposure to anticancer drugs reflect the changes in the cellular compositions which are correlated to the modes of action of drugs. In the present study, MCF7 cells (an estrogen receptor expressing breast cancer cell line) were exposed to three anticancer drugs belonging to two well-characterized anticancer classes: selective estrogen receptor modulators (SERMs) and DNA-intercalating agent. First, we evaluated if the changes in the spectrum of cells are according to the modes of action of drugs and the characteristics of the MCF7 cell line in the same way as the MDA-MB-231 cell. Living MCF7 cells were treated in the three drugs at half maximal inhibitory concentration (IC50), and the difference spectra were analyzed using principal component analysis (PCA). The results demonstrated clear separation between tamoxifen/toremifene (SERM)-treated cells from the doxorubicin (DNA-intercalator)-treated and untreated cells (control). Tamoxifen and toremifene induced similar spectral changes in the cellular compositions of MCF7 cells and lead to the clustering of these two drugs in the same quadrant of the principal component 1 (PC1) versus PC2 score plots. The separation is mostly attributed to their similar modes of actions. However, doxorubicin-treated MCF7 cells highlighted spectral changes that mainly occur in bands at 1085 and 1200-1240 cm^-1, which could be associated with the DNA-intercalation effects of the drug. Second, the pairwise PCA at various individual time points was employed to investigate whether the spectral changes of MCF7 and MDA-MB-231 cells in response to the IC50 of tamoxifen/toremifene and doxorubicin are dependent on the characteristics of the cell lines. The estrogen-expressing MCF7 cells demonstrated significant differences in response to the SERMs in comparison to the triple negative MDA-MB-231 cells, suggesting that different modes of action have taken place in the two tested cell lines. In contrast, the doxorubicin-treated MDA-MB-231 and MCF7 cells show similar changes in 1150-950 cm^-1, which indicates that the DNA intercalation effect of doxorubicin is found in both cell lines. The results have demonstrated that live-cell FTIR analysis is sensitive to the different modes of action from the same drugs on cells with different characteristics.

Human Pluripotent Stem Cell-Derived Cardiomyocytes for Assessment of Anticancer Drug-Induced Cardiotoxicity

Cardiotoxicity is a major cause of high attrition rates among newly developed drugs. Moreover, anti-cancer treatment-induced cardiotoxicity is one of the leading reasons of mortality in cancer survivors. Cardiotoxicity screening in vitro may improve predictivity of cardiotoxicity by novel drugs, using human pluripotent stem cell (hPSC)-derived-cardiomyocytes. Anthracyclines, including Doxorubicin, are widely used and highly effective chemotherapeutic agents for the treatment of different forms of malignancies. Unfortunately, anthracyclines cause many cardiac complications early or late after therapy. Anthracyclines exhibit their potent anti-cancer effect primarily via induction of DNA damage during the DNA replication phase in proliferative cells. In contrast, studies in animals and hPSC-cardiomyocytes have revealed that cardiotoxic effects particularly arise from (1) the generation of oxidative stress inducing mitochondrial dysfunction, (2) disruption of calcium homeostasis, and (3) changes in transcriptome and proteome, triggering apoptotic cell death. To increase the therapeutic index of chemotherapeutic Doxorubicin therapy several protective strategies have been developed or are under development, such as (1) reducing toxicity through modification of Doxorubicin (analogs), (2) targeted delivery of anthracyclines specifically to the tumor tissue or (3) cardioprotective agents that can be used in combination with Doxorubicin. Despite continuous progress in the field of cardio-oncology, cardiotoxicity is still one of the major complications of anti-cancer therapy. In this review, we focus on current hPSC-cardiomyocyte models for assessing anthracycline-induced cardiotoxicity and strategies for cardioprotection. In addition, we discuss latest developments toward personalized advanced pre-clinical models that are more closely recapitulating the human heart, which are necessary to support in vitro screening platforms with higher predictivity. These advanced models have the potential to reduce the time from bench-to-bedside of novel antineoplastic drugs with reduced cardiotoxicity.

The Biology of Malignant Mesothelioma and the Relevance of Preclinical Models

Malignant mesothelioma (MM), especially its more frequent form, malignant pleural mesothelioma (MPM), is a devastating thoracic cancer with limited therapeutic options. Recently, clinical trials that used immunotherapy strategies have yielded promising results, but the benefits are restricted to a limited number of patients. To develop new therapeutic strategies and define predictors of treatment response to existing therapy, better knowledge of the cellular and molecular mechanisms of MM tumors and sound preclinical models are needed. This review aims to provide an overview of our present knowledge and issues on both subjects. MM shows a complex pattern of molecular changes, including genetic, chromosomic, and epigenetic alterations. MM is also a heterogeneous cancer. The recently described molecular classifications for MPM could better consider inter-tumor heterogeneity, while histo-molecular gradients are an interesting way to consider both intra- and inter-tumor heterogeneities. Classical preclinical models are based on use of MM cell lines in culture or implanted in rodents, i.e., xenografts in immunosuppressed mice or isografts in syngeneic rodents to assess the anti-tumor immune response. Recent developments are tumoroids, patient-derived xenografts (PDX), xenografts in humanized mice, and genetically modified mice (GEM) that carry mutations identified in human MM tumor cells. Multicellular tumor spheroids are an interesting in vitro model to reduce animal experimentation; they are more accessible than tumoroids. They could be relevant, especially if they are co-cultured with stromal and immune cells to partially reproduce the human microenvironment. Even if preclinical models have allowed for major advances, they show several limitations: (i) the anatomical and biological tumor microenvironments are incompletely reproduced; (ii) the intra-tumor heterogeneity and immunological contexts are not fully reconstructed; and (iii) the inter-tumor heterogeneity is insufficiently considered. Given that these limitations vary according to the models, preclinical models must be carefully selected depending on the objectives of the experiments. New approaches, such as organ-on-a-chip technologies or in silico biological systems, should be explored in MM research. More pertinent cell models, based on our knowledge on mesothelial carcinogenesis and considering MM heterogeneity, need to be developed. These endeavors are mandatory to implement efficient precision medicine for MM.

Patient-derived explants (PDEs) as a powerful preclinical platform for anti-cancer drug and biomarker discovery

Preclinical models that can accurately predict outcomes in the clinic are much sought after in the field of cancer drug discovery and development. Existing models such as organoids and patient-derived xenografts have many advantages, but they suffer from the drawback of not contextually preserving human tumour architecture. This is a particular problem for the preclinical testing of immunotherapies, as these agents require an intact tumour human-specific microenvironment for them to be effective. In this review, we explore the potential of patient-derived explants (PDEs) for fulfilling this need. PDEs involve the ex vivo culture of fragments of freshly resected human tumours that retain the histological features of original tumours. PDE methodology for anti-cancer drug testing has been in existence for many years, but the platform has not been widely adopted in translational research facilities, despite strong evidence for its clinical predictivity. By modifying PDE endpoint analysis to include the spatial profiling of key biomarkers by using multispectral imaging, we argue that PDEs offer many advantages, including the ability to correlate drug responses with tumour pathology, tumour heterogeneity and changes in the tumour microenvironment. As such, PDEs are a powerful model of choice for cancer drug and biomarker discovery programmes.

ZeOncoTest: Refining and Automating the Zebrafish Xenograft Model for Drug Discovery in Cancer

The xenograft of human cancer cells in model animals is a powerful tool for understanding tumor progression and metastatic potential. Mice represent a validated host, but their use is limited by the elevated experimental costs and low throughput. To overcome these restrictions, zebrafish larvae might represent a valuable alternative. Their small size and transparency allow the tracking of transplanted cells. Therefore, tumor growth and early steps of metastasis, which are difficult to evaluate in mice, can be addressed. In spite of its advantages, the use of this model has been hindered by lack of experimental homogeneity and validation. Considering these facts, the aim of our work was to standardize, automate, and validate a zebrafish larvae xenograft assay with increased translatability and higher drug screening throughput. The ZeOncoTest reliability is based on the optimization of different experimental parameters, such as cell labeling, injection site, automated individual sample image acquisition, and analysis. This workflow implementation finally allows a higher precision and experimental throughput increase, when compared to previous reports. The approach was validated with the breast cancer cell line MDA-MB-231, the colorectal cancer cells HCT116, and the prostate cancer cells PC3; and known drugs, respectively RKI-1447, Docetaxel, and Mitoxantrone. The results recapitulate growth and invasion for all tested tumor cells, along with expected efficacy of the compounds. Finally, the methodology has proven useful for understanding specific drugs mode of action. The insights gained bring a step further for zebrafish larvae xenografts to enter the regulated preclinical drug discovery path.

Long-term monitoring in a microfluidic system to study tumour spheroid response to chronic and cycling hypoxia

We demonstrate the application of a microfluidic platform combining spatiotemporal oxygen control and long-term microscopy monitoring to observe tumour spheroid response to hypoxia. The platform is capable of recreating physiologically-relevant low and cycling oxygen levels not attainable in traditional cell culture environments, while image-based monitoring visualizes cell response to these physiologically-relevant conditions. Monitoring spheroid cultures during hypoxic exposure allows us to observe, for the first time, that spheroids swell and shrink in response to time-varying oxygen profiles switching between 0% and 10% O₂; this swelling-shrinkage behaviour appears to be driven by swelling of individual cells within the spheroids. We also apply the system to monitoring tumour models during anticancer treatment under varying oxygen conditions. We observe higher uptake of the anticancer agent doxorubicin under a cycling hypoxia profile than under either chronic hypoxia or in vitro normoxia, and the two-photon microscopy monitoring facilitated by our system also allows us to observe heterogeneity in doxorubicin uptake within spheroids at the single-cell level. Combining optical sectioning microscopy with precise spatiotemporal oxygen control and 3D culture opens the door for a wide range of future studies on microenvironmental mechanisms driving cancer progression and resistance to anticancer therapy. These types of studies could facilitate future improvements in cancer diagnostics and treatment.

The Role of the Microbiome in Drug Response

The microbiome is known to regulate many aspects of host health and disease and is increasingly being recognized as a key mediator of drug action. However, investigating the complex multidirectional relationships between drugs, the microbiota, and the host is a challenging endeavor, and the biological mechanisms that underpin these interactions are often not well understood. In this review, we outline the current evidence that supports a role for the microbiota as a contributor to both the therapeutic benefits and side effects of drugs, with a particular focus on those used to treat mental disorders, type 2 diabetes, and cancer. We also provide a snapshot of the experimental and computational tools that are currently available for the dissection of drug-microbiota-host interactions. The advancement of knowledge in this area may ultimately pave the way for the development of novel microbiota-based strategies that can be used to improve treatment outcomes.

Assays for tyrosine phosphorylation in human cells

Tyrosine kinases are important for many cellular processes and disruption of their regulation is a factor in diseases like cancer, therefore they are a major target of anticancer drugs. There are many ways to measure tyrosine kinase activity in cells by monitoring endogenous substrate phosphorylation, or by using peptide substrates and incubating them with cell lysates containing active kinases. However, most of these strategies rely on antibodies and/or are limited in how accurately they model the intracellular environment. In cases in which activity needs to be measured in cells, but endogenous substrates are not known and/or suitable phosphospecific antibodies are not available, cell-deliverable peptide substrates can be an alternative and can provide information on activation and inhibition of kinases in intact, live cells. In this chapter, we review this methodology and provide a protocol for measuring Abl kinase activity in human cells using enzyme-linked immunosorbent assay (ELISA) with a generic antiphosphotyrosine antibody for detection.

The role of mouse tumour models in the discovery and development of anticancer drugs

Our understanding of cancer biology has increased substantially over the past 30 years. Despite this, and an increasing pharmaceutical company expenditure on research and development, the approval of novel oncology drugs during the past decade continues to be modest. In addition, the attrition of agents during clinical development remains high. This attrition can be attributed, at least in part, to the clinical development being underpinned by the demonstration of predictable efficacy in experimental models of human tumours. This review will focus on the range of models available for the discovery and development of anticancer drugs, from traditional subcutaneous injection of tumour cell lines to mice genetically engineered to spontaneously give rise to tumours. It will consider the best time to use the models, along with practical applications and shortcomings. Finally, and most importantly, it will describe how these models reflect the underlying cancer biology and how well they predict efficacy in the clinic. Developing a line of sight to the clinic early in a drug discovery project provides clear benefit, as it helps to guide the selection of appropriate preclinical models and facilitates the investigation of relevant biomarkers.

Cancer drug development: The missing links

IMPACT STATEMENT

The success rate for cancer drugs which enter into phase 1 clinical trials is utterly less. Why the vast majority of drugs fail is not understood but suggests that pre-clinical studies are not adequate for human diseases. In 1975, as per the Tufts Center for the Study of Drug Development, pharmaceutical industries expended 100 million dollars for research and development of the average FDA approved drug. By 2005, this figure had more than quadrupled, to $1.3 billion. In order to recover their high and risky investment cost, pharmaceutical companies charge more for their products. However, there exists no correlation between drug development cost and actual sale of the drug. This high drug development cost could be due to the reason that all patients might not respond to the drug. Hence, a given drug has to be tested in large number of patients to show drug benefits and obtain significant results.

Inventing new therapies without reinventing the wheel: the power of drug repurposing

LINKED ARTICLES

This article is part of a themed section on Inventing New Therapies Without Reinventing the Wheel: The Power of Drug Repurposing. To view the other articles in this section visit http://onlinelibrary.wiley.com/doi/10.1111/bph.v175.2/issuetoc.

Precision Medicine with Imprecise Therapy: Computational Modeling for Chemotherapy in Breast Cancer

Medical oncology is in need of a mathematical modeling toolkit that can leverage clinically-available measurements to optimize treatment selection and schedules for patients. Just as the therapeutic choice has been optimized to match tumor genetics, the delivery of those therapeutics should be optimized based on patient-specific pharmacokinetic/pharmacodynamic properties. Under the current approach to treatment response planning and assessment, there does not exist an efficient method to consolidate biomarker changes into a holistic understanding of treatment response. While the majority of research on chemotherapies focus on cellular and genetic mechanisms of resistance, there are numerous patient-specific and tumor-specific measures that contribute to treatment response. New approaches that consolidate multimodal information into actionable data are needed. Mathematical modeling offers a solution to this problem. In this perspective, we first focus on the particular case of breast cancer to highlight how mathematical models have shaped the current approaches to treatment. Then we compare chemotherapy to radiation therapy. Finally, we identify opportunities to improve chemotherapy treatments using the model of radiation therapy. We posit that mathematical models can improve the application of anticancer therapeutics in the era of precision medicine. By highlighting a number of historical examples of the contributions of mathematical models to cancer therapy, we hope that this contribution serves to engage investigators who may not have previously considered how mathematical modeling can provide real insights into breast cancer therapy.

Human tissue models in cancer research: looking beyond the mouse

Mouse models, including patient-derived xenograft mice, are widely used to address questions in cancer research. However, there are documented flaws in these models that can result in the misrepresentation of human tumour biology and limit the suitability of the model for translational research. A coordinated effort to promote the more widespread development and use of ‘non-animal human tissue’ models could provide a clinically relevant platform for many cancer studies, maximising the opportunities presented by human tissue resources such as biobanks. A number of key factors limit the wide adoption of non-animal human tissue models in cancer research, including deficiencies in the infrastructure and the technical tools required to collect, transport, store and maintain human tissue for lab use. Another obstacle is the long-standing cultural reliance on animal models, which can make researchers resistant to change, often because of concerns about historical data compatibility and losing ground in a competitive environment while new approaches are embedded in lab practice. There are a wide range of initiatives that aim to address these issues by facilitating data sharing and promoting collaborations between organisations and researchers who work with human tissue. The importance of coordinating biobanks and introducing quality standards is gaining momentum. There is an exciting opportunity to transform cancer drug discovery by optimising the use of human tissue and reducing the reliance on potentially less predictive animal models.

Summary: Samuel Jackson and Gareth Thomas discuss the limitations of patient-derived xenograft mouse models and highlight initiatives to maximise the use of human tissue in cancer research, with the goal of improving translation and reducing animal experimentation.

PKIDB: A Curated, Annotated and Updated Database of Protein Kinase Inhibitors in Clinical Trials

The number of protein kinase inhibitors (PKIs) approved worldwide continues to grow steadily, with 39 drugs approved in the period between 2001 and January 2018. PKIs on the market have been the subject of many reviews, and structure-property relationships specific to this class of drugs have been inferred. However, the large number of PKIs under development is often overlooked. In this paper, we present PKIDB (Protein Kinase Inhibitor Database), a monthly-updated database gathering approved PKIs as well as PKIs currently in clinical trials. The database compiles currently 180 inhibitors ranging from phase 0 to 4 clinical trials along with annotations extracted from seven public resources. The distribution and property ranges of standard physicochemical properties are presented. They can be used as filters to better prioritize compound selection for future screening campaigns. Interestingly, more than one-third of the kinase inhibitors violate at least one Lipinski’s rule. A Principal Component Analysis (PCA) reveals that Type-II inhibitors are mapped to a distinct chemical space as compared to orally administrated drugs as well as to other types of kinase inhibitors. Using a Principal Moment of Inertia (PMI) analysis, we show that PKIs under development tend to explore new shape territories as compared to approved PKIs. In order to facilitate the analysis of the protein space, the kinome tree has been annotated with all protein kinases being targeted by PKIs. Finally, we analyzed the pipeline of the pharmaceutical companies having PKIs on the market or still under development. We hope that this work will assist researchers in the kinase field in identifying and designing the next generation of kinase inhibitors for still untargeted kinases. The PKIDB database is freely accessible from a website at http://www.icoa.fr/pkidb and can be easily browsed through a user-friendly spreadsheet-like interface.

New insight for pharmacogenomics studies from the transcriptional analysis of two large-scale cancer cell line panels

One of the most challenging problems in the development of new anticancer drugs is the very high attrition rate. The so-called “drug repositioning process” propose to find new therapeutic indications to already approved drugs. For this, new analytic methods are required to optimize the information present in large-scale pharmacogenomics datasets. We analyzed data from the Genomics of Drug Sensitivity in Cancer and Cancer Cell Line Encyclopedia studies. We focused on common cell lines (n = 471), considering the molecular information, and the drug sensitivity for common drugs screened (n = 15). We propose a novel classification based on transcriptomic profiles of cell lines, according to a biological network-driven gene selection process. Our robust molecular classification displays greater homogeneity of drug sensitivity than cancer cell line grouped based on tissue of origin. We then identified significant associations between cell line cluster and drug response robustly found between both datasets. We further demonstrate the relevance of our method using two additional external datasets and distinct sensitivity metrics. Some associations were still found robust, despite cell lines and drug responses’ variations. This study defines a robust molecular classification of cancer cell lines that could be used to find new therapeutic indications to known compounds.

Small cell lung cancer: model of circulating tumor cell tumorospheres in chemoresistance

Small cell lung cancer (SCLC) represents 15% of lung cancers and is characterized by early dissemination, development of chemoresistance and a poor prognosis. A host of diverse drugs failed invariably and its mechanisms of global chemoresistance have not been characterized so far. SCLC represents the prototype of an aggressive and highly metastatic tumor which is ultimately refractory to any treatment. High numbers of circulating tumor cells (CTCs) allowed us to establish 5 CTC cell lines (BHGc7, 10, 16, 26 and UHGc5) from patients with recurrent SCLC. These cell lines exhibit the typical SCLC markers and CTCs of all patients developed spontaneously large multicellular aggregates, termed tumorospheres. Ki67 and carbonic anhydrase 9 (CAIX) staining of tumorosphere sections revealed quiescent and hypoxic cells, respectively. Accordingly, comparison of the chemosensitivity of CTC single cell suspensions with tumorospheres demonstrated increased resistance of the clusters against chemotherapeutics commonly used for treatment of SCLC. Therefore, global chemoresistance of relapsing SCLC seems to rely on formation of large tumorospheres which reveal limited accessibility, lower growth fraction and hypoxic conditions. Since similar tumor spheroids were found in other tumor types, SCLC seems to represent a unique tumor model to study the association of CTCs, metastasis and drug resistance.

A Pathway to Personalizing Therapy for Metastases Using Liver-on-a-Chip Platforms

Metastasis accounts for most cancer-related deaths. The majority of solid cancers, including those of the breast, colorectum, prostate and skin, metastasize at significant levels to the liver due to its hemodynamic as well as tumor permissive microenvironmental properties. As this occurs prior to detection and treatment of the primary tumor, we need to target liver metastases to improve patients' outcomes. Animal models, while proven to be useful in mechanistic studies, do not represent the heterogeneity of human population especially in drug metabolism lack proper human cell-cell interactions, and this gap between animals and humans results in costly and inefficient drug discovery. This underscores the need to accurately model the human liver for disease studies and drug development. Further, the occurrence of liver metastases is influenced by the primary tumor type, sex and race; thus, modeling these specific settings will facilitate the development of personalized/targeted medicine for each specific group. We have adapted such all-human 3D ex vivo hepatic microphysiological system (MPS) (a.k.a. liver-on-a-chip) to investigate human micrometastases. This review focuses on the sources of liver resident cells, especially the iPS cell-derived hepatocytes, and examines some of the advantages and disadvantages of these sources. In addition, this review also examines other potential challenges and limitations in modeling human liver.

[Early clinical trials in paediatric oncology in Spain: a nationwide perspective]

INTRODUCTION

Cancer is the leading cause of death between the first year of life and adolescence, and some types of diseases are still a major challenge in terms of cure. There is, therefore, a major need for new drugs. Recent findings in cancer biology open the door to the development of targeted therapies against individual molecular changes, as well as immunotherapy. Promising results in adult anti-cancer drug development have not yet been translated into paediatric clinical practice. A report is presented on the activity in early paediatric oncology trials (phase I-II) in Spain.

MATERIAL AND METHODS

All members of the Spanish Society of Paediatric Haematology Oncology (SEHOP) were contacted in order to identify early clinical trials in paediatric cancer opened between 2005 and 2015.

RESULTS

A total of 30 trials had been opened in this period: 21 (70%) in solid tumours, and 9 (30%) in malignant haemopathies. A total of 212 patients have been enrolled. The majority was industry sponsored (53%). Since 2010, four centres have joined the international consortium of Innovative Therapies for Children with Cancer (ITCC), which has as its aim to develop novel therapies for paediatric tumours. A significant number of new studies have opened since 2010, improving the treatment opportunities for our children. Results of recently closed trials show the contribution of Spanish investigators, the introduction of molecularly targeted agents, and their benefits.

CONCLUSIONS

The activity in clinical trials has increased in the years analysed. The SEHOP is committed to develop and participate in collaborative academic trials, in order to help in the advancement and optimisation of existing therapies in paediatric cancer.

Validation of Mycobacterium tuberculosis dihydroneopterin aldolase as a molecular target for anti-tuberculosis drug development

An early step of target validation in antimicrobial drug discovery is to prove that a gene coding for a putative target is essential for pathogen's viability. However, little attention has been paid to demonstrate the causal links between gene essentiality and a particular protein function that will be the focus of a drug discovery effort. This should be considered an important step in target validation since a growing number of proteins are found to exhibit multiple and unrelated tasks. Here, we show that the Mycobacterium tuberculosis (Mtb) folB gene is essential and that this essentiality depends on the dihydroneopterin aldolase/epimerase activities of its protein product, the FolB protein from the folate biosynthesis pathway. The wild-type (WT) MtFolB and point mutants K99A and Y54F were cloned, expressed, purified and monitored for the aldolase, epimerase and oxygenase activities using HPLC. In contrast to the WT MtFolB, both mutants have neither aldolase nor epimerase activities in the conditions assayed. We then performed gene knockout experiments and showed that folB gene is essential for Mtb survival under the conditions tested. Moreover, only the WT folB sequence could be used as a rescue copy in gene complementation studies. When the sequences of mutants K99A or Y54F were used for complementation, no viable colonies were obtained, indicating that aldolase and/or epimerase activities are crucial for Mtb survival. These results provide a solid basis for further work aiming to develop new anti-TB agents acting as inhibitors of the aldolase/epimerase activities of MtFolB.