Rapid optimization of drug combinations for the optimal angiostatic treatment of cancer

Using DeepSignalingFlow to mine signaling flows interpreting mechanism of synergy of cocktails

Complex signaling pathways are believed to be responsible for drug resistance. Drug combinations perturbing multiple signaling targets have the potential to reduce drug resistance. The large-scale multi-omic datasets and experimental drug combination synergistic score data are valuable resources to study mechanisms of synergy (MoS) to guide the development of precision drug combinations. However, signaling patterns of MoS are complex and remain unclear, and thus it is challenging to identify synergistic drug combinations in clinical. Herein, we proposed a novel integrative and interpretable graph AI model, DeepSignalingFlow, to uncover the MoS by integrating and mining multi-omic data. The major innovation is that we uncover MoS by modeling the signaling flow from multi-omic features of essential disease proteins to the drug targets, which has not been introduced by the existing models. The model performance was assessed utilizing four distinct drug combination synergy evaluation datasets, i.e., NCI ALMANAC, O'Neil, DrugComb, and DrugCombDB. The comparison results showed that the proposed model outperformed existing graph AI models in terms of synergy score prediction, and can interpret MoS using the core signaling flows. The code is publicly accessible via Github: https://github.com/FuhaiLiAiLab/DeepSignalingFlow.

Blood Vessel-Targeted Therapy in Colorectal Cancer: Current Strategies and Future Perspectives

The vasculature is a key player and regulatory component in the multicellular microenvironment of solid tumors and, consequently, a therapeutic target. In colorectal carcinoma (CRC), antiangiogenic treatment was approved almost 20 years ago, but there are still no valid predictors of response. In addition, treatment resistance has become a problem. Vascular heterogeneity and plasticity due to species-, organ-, and milieu-dependent phenotypic and functional differences of blood vascular cells reduced the hope of being able to apply a standard approach of antiangiogenic therapy to all patients. In addition, the pathological vasculature in CRC is characterized by heterogeneous perfusion, impaired barrier function, immunosuppressive endothelial cell anergy, and metabolic competition-induced microenvironmental stress. Only recently, angiocrine proteins have been identified that are specifically released from vascular cells and can regulate tumor initiation and progression in an autocrine and paracrine manner. In this review, we summarize the history and current strategies for applying antiangiogenic treatment and discuss the associated challenges and opportunities, including normalizing the tumor vasculature, modulating milieu-dependent vascular heterogeneity, and targeting functions of angiocrine proteins. These new strategies could open perspectives for future vascular-targeted and patient-tailored therapy selection in CRC.

Complexes of Ruthenium(II) as Promising Dual-Active Agents against Cancer and Viral Infections

Poor responses to medical care and the failure of pharmacological treatment for many high-frequency diseases, such as cancer and viral infections, have been widely documented. In this context, numerous metal-based substances, including cisplatin, auranofin, various gold metallodrugs, and ruthenium complexes, are under study as possible anticancer and antiviral agents. The two Ru(III) and Ru(II) complexes, namely, BOLD-100 and RAPTA-C, are presently being studied in a clinical trial and preclinical studies evaluation, respectively, as anticancer agents. Interestingly, BOLD-100 has also recently demonstrated antiviral activity against SARS-CoV-2, which is the virus responsible for the COVID-19 pandemic. Over the last years, much effort has been dedicated to discovering new dual anticancer-antiviral agents. Ru-based complexes could be very suitable in this respect. Thus, this review focuses on the most recent studies regarding newly synthesized Ru(II) complexes for use as anticancer and/or antiviral agents.

CURATE.AI COR-Tx platform as a digital therapy and digital diagnostic for cognitive function in patients with brain tumour postradiotherapy treatment: protocol for a prospective mixed-methods feasibility clinical trial

INTRODUCTION

Conventional interventional modalities for preserving or improving cognitive function in patients with brain tumour undergoing radiotherapy usually involve pharmacological and/or cognitive rehabilitation therapy administered at fixed doses or intensities, often resulting in suboptimal or no response, due to the dynamically evolving patient state over the course of disease. The personalisation of interventions may result in more effective results for this population. We have developed the CURATE.AI COR-Tx platform, which combines a previously validated, artificial intelligence-derived personalised dosing technology with digital cognitive training.

METHODS AND ANALYSIS

This is a prospective, single-centre, single-arm, mixed-methods feasibility clinical trial with the primary objective of testing the feasibility of the CURATE.AI COR-Tx platform intervention as both a digital intervention and digital diagnostic for cognitive function. Fifteen patient participants diagnosed with a brain tumour requiring radiotherapy will be recruited. Participants will undergo a remote, home-based 10-week personalised digital intervention using the CURATE.AI COR-Tx platform three times a week. Cognitive function will be assessed via a combined non-digital cognitive evaluation and a digital diagnostic session at five time points: preradiotherapy, preintervention and postintervention and 16-weeks and 32-weeks postintervention. Feasibility outcomes relating to acceptability, demand, implementation, practicality and limited efficacy testing as well as usability and user experience will be assessed at the end of the intervention through semistructured patient interviews and a study team focus group discussion at study completion. All outcomes will be analysed quantitatively and qualitatively.

ETHICS AND DISSEMINATION

This study has been approved by the National Healthcare Group (NHG) DSRB (DSRB2020/00249). We will report our findings at scientific conferences and/or in peer-reviewed journals.

TRIAL REGISTRATION NUMBER

NCT04848935.

Pathological angiogenesis: mechanisms and therapeutic strategies

In multicellular organisms, angiogenesis, the formation of new blood vessels from pre-existing ones, is an essential process for growth and development. Different mechanisms such as vasculogenesis, sprouting, intussusceptive, and coalescent angiogenesis, as well as vessel co-option, vasculogenic mimicry and lymphangiogenesis, underlie the formation of new vasculature. In many pathological conditions, such as cancer, atherosclerosis, arthritis, psoriasis, endometriosis, obesity and SARS-CoV-2(COVID-19), developmental angiogenic processes are recapitulated, but are often done so without the normal feedback mechanisms that regulate the ordinary spatial and temporal patterns of blood vessel formation. Thus, pathological angiogenesis presents new challenges yet new opportunities for the design of vascular-directed therapies. Here, we provide an overview of recent insights into blood vessel development and highlight novel therapeutic strategies that promote or inhibit the process of angiogenesis to stabilize, reverse, or even halt disease progression. In our review, we will also explore several additional aspects (the angiogenic switch, hypoxia, angiocrine signals, endothelial plasticity, vessel normalization, and endothelial cell anergy) that operate in parallel to canonical angiogenesis mechanisms and speculate how these processes may also be targeted with anti-angiogenic or vascular-directed therapies.

Dichloro Ru(II)-p-cymene-1,3,5-triaza-7-phosphaadamantane (RAPTA-C): A Case Study

The use of organometallic compounds to treat various phenotypes of cancer has attracted increased interest in recent decades. Organometallic compounds, which are transitional between conventional inorganic and organic materials, have outstanding and one-of-a-kind features that offer fresh insight into the development of inorganic medicinal chemistry. The therapeutic potential of ruthenium(II)-arene RAPTA-type compounds is being thoroughly investigated, specifically owing to the excellent antimetastatic property of the initial candidate RAPTA-C. This review gives a thorough analysis of this complex and its evolution as a potential anticancer drug candidate. The numerous mechanistic investigations of RAPTA-C are discussed, and they are connected to the macroscopic biological characteristics that have been found. The "multitargeted" complex described here target enzymes, peptides, and intracellular proteins in addition to DNA that allow it to specifically target cancer cells. Understanding these may allow researchers to find specific targets and tune a new-generation organometallic complex accordingly.

Personalized Tacrolimus Dosing After Liver Transplantation: A Randomized Clinical Trial

Background

Inter- and intra-individual variability in tacrolimus dose requirements mandates empirical clinician-titrated dosing that frequently results in deviation from a narrow target range. Improved methods to individually dose tacrolimus are needed. Our objective was to determine whether a quantitative, dynamically-customized, phenotypic-outcome-guided dosing method termed Phenotypic Personalized Medicine (PPM) would improve target drug trough maintenance.

Methods

In a single-center, randomized, pragmatic clinical trial ( NCT03527238 ), 62 adults were screened, enrolled, and randomized prior to liver transplantation 1:1 to standard-of-care (SOC) clinician-determined or PPM-guided dosing of tacrolimus. The primary outcome measure was percent days with large (>2 ng/mL) deviation from target range from transplant to discharge. Secondary outcomes included percent days outside-of-target-range and mean area-under-the-curve (AUC) outside-of-target-range per day. Safety measures included rejection, graft failure, death, infection, nephrotoxicity, or neurotoxicity.

Results

56 (29 SOC, 27 PPM) patients completed the study. The primary outcome measure was found to be significantly different between the two groups. Patients in the SOC group had a mean of 38.4% of post-transplant days with large deviations from target range; the PPM group had 24.3% of post-transplant days with large deviations; (difference -14.1%, 95% CI: -26.7 to -1.5 %, P=0.029). No significant differences were found in the secondary outcomes. In post-hoc analysis, the SOC group had a 50% longer median length-of-stay than the PPM group [15 days (Q1-Q3: 11-20) versus 10 days (Q1-Q3: 8.5-12); difference 5 days, 95% CI: 2-8 days, P=0.0026].

Conclusions

PPM guided tacrolimus dosing leads to better drug level maintenance than SOC. The PPM approach leads to actionable dosing recommendations on a day-to-day basis.

Lay Summary

In a study on 62 adults who underwent liver transplantation, researchers investigated whether a new dosing method called Phenotypic Personalized Medicine (PPM) would improve daily dosing of the immunosuppression drug tacrolimus. They found that PPM guided tacrolimus dosing leads to better drug level maintenance than the standard-of-care clinician-determined dosing. This means that the PPM approach leads to actionable dosing recommendations on a day-to-day basis and can help improve patient outcomes.

Metal-Based Complexes in Cancer

Metal-based drugs have attracted growing interest in biomedicine [...].

Platform combining statistical modeling and patient-derived organoids to facilitate personalized treatment of colorectal carcinoma

BACKGROUND

We propose a new approach for designing personalized treatment for colorectal cancer (CRC) patients, by combining ex vivo organoid efficacy testing with mathematical modeling of the results.

METHODS

The validated phenotypic approach called Therapeutically Guided Multidrug Optimization (TGMO) was used to identify four low-dose synergistic optimized drug combinations (ODC) in 3D human CRC models of cells that are either sensitive or resistant to first-line CRC chemotherapy (FOLFOXIRI). Our findings were obtained using second order linear regression and adaptive lasso.

RESULTS

The activity of all ODCs was validated on patient-derived organoids (PDO) from cases with either primary or metastatic CRC. The CRC material was molecularly characterized using whole-exome sequencing and RNAseq. In PDO from patients with liver metastases (stage IV) identified as CMS4/CRIS-A, our ODCs consisting of regorafenib [1 mM], vemurafenib [11 mM], palbociclib [1 mM] and lapatinib [0.5 mM] inhibited cell viability up to 88%, which significantly outperforms FOLFOXIRI administered at clinical doses. Furthermore, we identified patient-specific TGMO-based ODCs that outperform the efficacy of the current chemotherapy standard of care, FOLFOXIRI.

CONCLUSIONS

Our approach allows the optimization of patient-tailored synergistic multi-drug combinations within a clinically relevant timeframe.

Network-informed discovery of multidrug combinations for ERα+/HER2-/PI3Kα-mutant breast cancer

Breast cancer is a persistent threat to women worldwide. A large proportion of breast cancers are dependent on the estrogen receptor α (ERα) for tumor progression. Therefore, targeting ERα with antagonists, such as tamoxifen, or estrogen deprivation by aromatase inhibitors remain standard therapies for ERα + breast cancer. The clinical benefits of monotherapy are often counterbalanced by off-target toxicity and development of resistance. Combinations of more than two drugs might be of great therapeutic value to prevent resistance, and to reduce doses, and hence, decrease toxicity. We mined data from the literature and public repositories to construct a network of potential drug targets for synergistic multidrug combinations. With 9 drugs, we performed a phenotypic combinatorial screen with ERα + breast cancer cell lines. We identified two optimized low-dose combinations of 3 and 4 drugs of high therapeutic relevance to the frequent ERα + /HER2-/PI3Kα-mutant subtype of breast cancer. The 3-drug combination targets ERα in combination with PI3Kα and cyclin-dependent kinase inhibitor 1 (p21). In addition, the 4-drug combination contains an inhibitor for poly (ADP-ribose) polymerase 1 (PARP1), which showed benefits in long-term treatments. Moreover, we validated the efficacy of the combinations in tamoxifen-resistant cell lines, patient-derived organoids, and xenograft experiments. Thus, we propose multidrug combinations that have the potential to overcome the standard issues of current monotherapies.

Discovery and Development of Tumor Angiogenesis Assays

The angiogenesis process was described in its basic concepts in the works of the Scottish surgeon John Hunter and terminologically assessed in the early twentieth century. An aberrant angiogenesis is a prerequisite for cancer cells in solid tumors to grow and metastasize. The sprouting of new blood vessels is one of the major characteristics of cancer and represents a gateway for tumor cells to enter both the blood and lymphatic circulation systems. In vivo, ex vivo, and in vitro models of angiogenesis have provided essential tools for cancer research and antiangiogenic drug screening. Several in vivo studies have been performed to investigate the various steps of tumor angiogenesis and in vitro experiments contributed to dissecting the molecular bases of this phenomenon. Moreover, coculture of cancer and endothelial cells in 2D and 3D matrices have contributed to improve the recapitulation of the complex process of tumor angiogenesis, including the peculiar conditions of tumor microenvironment.

Competitive binding studies of the nucleosomal histone targeting drug, [Ru(η6-p-cymene)Cl2(pta)] (RAPTA-C), with oligonucleotide-peptide mixtures

Protein crystallography and biochemical assays reveal that the organometallic drug, [Ru(η⁶-p-cymene)Cl₂(pta)] (RAPTA-C), preferentially binds to nucleosomal histone proteins in chromatin. To better understand the binding mechanism we report here a mass spectrometric-based competitive binding study between a model peptide from the acidic patch region of the H2A histone protein (the region where RAPTA-C is known to bind) and an oligonucleotide. In contrast to the protein crystallography and biochemical assays, RAPTA-C preferentially binds to the oligonucleotide, confirming that steric factors, rather than electronic effects, primarily dictate binding of RAPTA-C to histone proteins within the nucleosome.

3D In Vivo Models for Translational Research on Pancreatic Cancer: The Chorioallantoic Membrane (CAM) Model

Simple Summary

The 5-year overall survival rate for all stages of pancreatic cancer is relatively low at about only 6%. As a result of this exceedingly poor prognosis, new research models are necessary to investigate this highly malignant cancer. One model that has been used extensively for a vast variety of different cancers is the chorioallantoic membrane (CAM) model. It is based on an exceptionally vascularized membrane that develops within fertilized chicken eggs and can be used for the grafting and analysis of tumor tissue. The aim of the study was to summarize already existing works on pancreatic ductal adenocarcinoma (PDAC) and the CAM model. The results were subdivided into different categories that include drug testing, angiogenesis, personalized medicine, modifications of the model, and further developments to help improve the unfavorable prognosis of this disease.

Abstract

Pancreatic ductal adenocarcinoma (PDAC) is a highly aggressive cancer with adverse outcomes that have barely improved over the last decade. About half of all patients present with metastasis at the time of diagnosis, and the 5-year overall survival rate across all stages is only 6%. Innovative in vivo research models are necessary to combat this cancer and to discover novel treatment strategies. The chorioallantoic membrane (CAM) model represents one 3D in vivo methodology that has been used in a large number of studies on different cancer types for over a century. This model is based on a membrane formed within fertilized chicken eggs that contain a dense network of blood vessels. Because of its high cost-efficiency, simplicity, and versatility, the CAM model appears to be a highly valuable research tool in the pursuit of gaining more in-depth insights into PDAC. A summary of the current literature on the usage of the CAM model for the investigation of PDAC was conducted and subdivided into angiogenesis, drug testing, modifications, personalized medicine, and further developments. On this comprehensive basis, further research should be conducted on PDAC in order to improve the abysmal prognosis of this malignant disease.

A Face-To-Face Comparison of Tumor Chicken Chorioallantoic Membrane (TCAM) In Ovo with Murine Models for Early Evaluation of Cancer Therapy and Early Drug Toxicity

Ethical considerations, cost, and time constraints have highlighted the need to develop alternatives to rodent in vivo models for evaluating drug candidates for cancer. The tumor chicken chorioallantoic membrane (TCAM) model provides an affordable and fast assay that permits direct visualization of tumor progression. Tumors from multiple species including rodents and human cell lines can be engrafted. In this study, we engrafted several tumor models onto the CAM and demonstrated that the TCAM model is an alternative to mouse models for preliminary cancer drug efficacy testing and toxicity analysis. Tumor cells were deposited onto CAM, and then grown for up to an additional 10 days before chronic treatments were administered. The drug response of anticancer therapies was screened in 12 tumor cell lines including glioblastoma, melanoma, breast, prostate, colorectal, liver, and lung cancer. Tumor-bearing eggs and tumor-bearing mice had a similar chemotherapy response (cisplatin and temozolomide) in four human and mouse tumor models. We also demonstrated that lethality observed in chicken embryos following chemotherapies such as cisplatin and cyclophosphamide were associated with corresponding side-effects in mice with body weight loss. According to our work, TCAM represents a relevant alternative model to mice in early preclinical oncology screening, providing insights for both the efficacy and the toxicity of anticancer drugs.

Metal-Based Complexes as Potential Anti-cancer Agents

Metal based therapy is no new in biomedical research. In early days the biggest limitation was the inequality among therapeutical and toxicological dosages. Ever since, Barnett Rosenberg discovered cisplatin, a new era has begun to treat cancer with metal complexes. Platinum complexes such as oxaliplatin, cisplatin, and carboplatin, seem to be the foundation of metal/s-based components to challenge malignancies. With an advancement in the biomolemoecular mechanism, researchers have started developing non-classical platinum-based complexes, where a different mechanistic approach of the complexes is observed towards the biomolecular target. Till date, larger number of metal/s-based complexes was synthesized by overhauling the present structures chemically by substituting the ligand or preparing the whole novel component with improved cytotoxic and safety profiles. Howsoever, due to elevated accentuation upon the therapeutic importance of metal/s-based components, a couple of those agents are at present on clinical trials and several other are in anticipating regulatory endorsement to enter the trial. This literature highlights the detailed heterometallic multinuclear components, primarily focusing on platinum, ruthenium, gold and remarks on possible stability, synergism, mechanistic studies and structure activity relationships.

The Chicken Embryo Chorioallantoic Membrane as an In Vivo Model for Photodynamic Therapy

For many decades the chicken embryo chorioallantoic membrane (CAM) has been used for research as an in vivo model in a large number of different fields, including toxicology, bioengineering, and cancer research. More specifically, the CAM is also a suitable and convenient model system in the field of photodynamic therapy (PDT), mainly due to the easy access of its membrane and the possibility of grafting or growing tumors on the membrane and, interestingly, to study the PDT effects on its dense vascular network. In addition, the CAM is simple to handle and cheap. Since the CAM is not innervated until later stages of the embryo development, its use in research is simplified compared to other in vivo models as far as ethical and regulatory issues are concerned. In this review different incubation and drug administration protocols of relevance for PDT are presented. Moreover, data regarding the propagation of light at different wavelengths and CAM development stages are provided. Finally, the effects induced by photobiomodulation on the CAM angiogenesis and its impact on PDT treatment outcome are discussed.

Microcrater-Arrayed Chemiluminescence Cell Chip to Boost Anti-Cancer Drug Administration in Zebrafish Tumor Xenograft Model

PURPOSE

The aim of this study was to develop a rapid and automatic drug screening platform using microcrater-arrayed (µCA) cell chips.

METHODS

The µCA chip was fabricated using a laser direct writing technique. The fabrication time required for one 9 × 9 microarray wax chip was as quick as 1 min. On a nanodroplet handling platform, the chip was pre-coated with anti-cancer drugs, including cyclophosphamide, cisplatin, doxorubicin, oncovin, etoposide, and 5-fluorouracil, and their associated mixtures. Cell droplets containing 100 SK-N-DZ or MCF-7 cells were then loaded onto the chip. Cell viability was examined directly through a chemiluminescence assay on the chip using the CellTiter-Glo assay.

RESULTS

The time needed for the drug screening assay was demonstrated to be less than 30 s for a total of 81 tests. The prediction of optimal drug synergy from the µCA chip was found by matching it to that of the zebrafish MCF-7 tumor xenograft model, instead of the conventional 96-well plate assay. In addition, the critical reagent volume and cell number for each µCA chip test were 200 nL and 100 cells, respectively, which were significantly lower than 100 µL and 4000 cells, which were achieved using the 96-well assay.

CONCLUSION

Our study for the µCA chip platform could improve the high-throughput drug synergy screening targeting the applications of tumor cell biology.

Personalised, Rational, Efficacy-Driven Cancer Drug Dosing via an Artificial Intelligence SystEm (PRECISE): A Protocol for the PRECISE CURATE.AI Pilot Clinical Trial

Introduction: Oncologists have traditionally administered the maximum tolerated doses of drugs in chemotherapy. However, these toxicity-guided doses may lead to suboptimal efficacy. CURATE.AI is an indication-agnostic, mechanism-independent and efficacy-driven personalised dosing platform that may offer a more optimal solution. While CURATE.AI has already been applied in a variety of clinical settings, there are no prior randomised controlled trials (RCTs) on CURATE.AI-guided chemotherapy dosing for solid tumours. Therefore, we aim to assess the technical and logistical feasibility of a future RCT for CURATE.AI-guided solid tumour chemotherapy dosing. We will also collect exploratory data on efficacy and toxicity, which will inform RCT power calculations.

Methods and analysis: This is an open-label, single-arm, two-centre, prospective pilot clinical trial, recruiting adults with metastatic solid tumours and raised baseline tumour marker levels who are planned for palliative-intent, capecitabine-based chemotherapy. As CURATE.AI is a small data platform, it will guide drug dosing for each participant based only on their own tumour marker levels and drug doses as input data. The primary outcome is the proportion of participants in whom CURATE.AI is successfully applied to provide efficacy-driven personalised dosing, as judged based on predefined considerations. Secondary outcomes include the timeliness of dose recommendations, participant and physician adherence to CURATE.AI-recommended doses, and the proportion of clinically significant dose changes. We aim to initially enrol 10 participants from two hospitals in Singapore, perform an interim analysis, and consider either cohort expansion or an RCT. Recruitment began in August 2020. This pilot clinical trial will provide key data for a future RCT of CURATE.AI-guided personalised dosing for precision oncology.

Ethics and dissemination: The National Healthcare Group (NHG) Domain Specific Review Board has granted ethical approval for this study (DSRB 2020/00334). We will distribute our findings at scientific conferences and publish them in peer-reviewed journals.

Trial registration number: NCT04522284

Drug Repurposing to Identify a Synergistic High-Order Drug Combination to Treat Sunitinib-Resistant Renal Cell Carcinoma

Simple Summary

In this study, drug combination screening was used to design a multidrug combination consisting of repurposed drugs to treat sunitinib-resistant clear cell renal cell carcinoma. In the frame of this project, the multidrug combination has been optimized and validated and an insight into the mechanism of action is given. The multidrug combinations significantly altered the transcription of genes related to apoptosis and metabolic pathways. Further analysis of the metabolism revealed strong upregulation of the presence of sphingolipids after multidrug combination treatment. Final evaluation for translation of the multidrug combination in ex vivo organoid-like cultures demonstrated significant anti-cancer efficacy.

Abstract

Repurposed drugs have been evaluated for the management of clear cell renal cell carcinoma (ccRCC), but only a few have influenced the overall survival of patients with advanced disease. To combine repurposed non-oncology with oncological drugs, we applied our validated phenotypic method, which consisted of a reduced experimental part and data modeling. A synergistic optimized multidrug combination (ODC) was identified to significantly reduce the energy levels in cancer remaining inactive in non-cancerous cells. The ODC consisted of Rapta-C, erlotinib, metformin and parthenolide and low doses. Molecular and functional analysis of ODC revealed a loss of adhesiveness and induction of apoptosis. Gene-expression network analysis displayed significant alterations in the cellular metabolism, confirmed by LC-MS based metabolomic analysis, highlighting significant changes in the lipid classes. We used heterotypic in vitro 3D co-cultures and ex vivo organoids to validate the activity of the ODC, maintaining an efficacy of over 70%. Our results show that repurposed drugs can be combined to target cancer cells selectively with prominent activity. The strong impact on cell adherence and metabolism indicates a favorable mechanism of action of the ODC to treat ccRCC.

Anti-angiogenic agents - overcoming tumour endothelial cell anergy and improving immunotherapy outcomes

Immune checkpoint inhibitors have revolutionized medical oncology, although currently only a subset of patients has a response to such treatment. A compelling body of evidence indicates that anti-angiogenic therapy has the capacity to ameliorate antitumour immunity owing to the inhibition of various immunosuppressive features of angiogenesis. Hence, combinations of anti-angiogenic agents and immunotherapy are currently being tested in >90 clinical trials and 5 such combinations have been approved by the FDA in the past few years. In this Perspective, we describe how the angiogenesis-induced endothelial immune cell barrier hampers antitumour immunity and the role of endothelial cell anergy as the vascular counterpart of immune checkpoints. We review the antitumour immunity-promoting effects of anti-angiogenic agents and provide an update on the current clinical successes achieved when these agents are combined with immune checkpoint inhibitors. Finally, we propose that anti-angiogenic agents are immunotherapies - and vice versa - and discuss future research priorities.

Optimized low-dose combinatorial drug treatment boosts selectivity and efficacy of colorectal carcinoma treatment

The current standard of care for colorectal cancer (CRC) is a combination of chemotherapeutics, often supplemented with targeted biological drugs. An urgent need exists for improved drug efficacy and minimized side effects, especially at late‐stage disease. We employed the phenotypically driven therapeutically guided multidrug optimization (TGMO) technology to identify optimized drug combinations (ODCs) in CRC. We identified low‐dose synergistic and selective ODCs for a panel of six human CRC cell lines also active in heterotypic 3D co‐culture models. Transcriptome sequencing and phosphoproteome analyses showed that the mechanisms of action of these ODCs converged toward MAP kinase signaling and cell cycle inhibition. Two cell‐specific ODCs were translated to in vivo mouse models. The ODCs reduced tumor growth by ~80%, outperforming standard chemotherapy (FOLFOX). No toxicity was observed for the ODCs, while significant side effects were induced in the group treated with FOLFOX therapy. Identified ODCs demonstrated significantly enhanced bioavailability of the individual components. Finally, ODCs were also active in primary cells from CRC patient tumor tissues. Taken together, we show that the TGMO technology efficiently identifies selective and potent low‐dose drug combinations, optimized regardless of tumor mutation status, outperforming conventional chemotherapy.

Oncometabolites lactate and succinate drive pro-angiogenic macrophage response in tumors

Macrophages are innate phagocytic leukocytes that are highly present in solid tumors, where they are referred to as tumor-associated macrophages (TAMs). In solid tumors, the microenvironment is often immunosuppressive and hypoxic regions are prevalent. These hypoxic conditions impose tumor cells to reprogram their metabolism, shifting from oxidative phosphorylation to anaerobic glycolysis. This so-called glycolytic switch enables hypoxic tumor cells to survive, proliferate, and eventually to outcompete untransformed cells. The hypoxia-induced change in tumor cell metabolism leads to the production of oncometabolites, among which are the glycolytic end-metabolite lactate and the tricarboxylic acid cycle intermediate succinate. TAMs can react to these oncometabolites, resulting in an altered maturation and the adoption of pro-angiogenic features. These angiogenesis-promoting TAMs have been reported to cooperate with tumor cells in the formation of new vessels, and even have been considered an important cause of resistance against anti-angiogenic therapies. For a long time, the mechanisms by which lactate and succinate activated pro-angiogenic TAMs were not understood. Researchers now start to unravel and understand some of the underlying mechanisms. Here, the importance of microenvironmental cues in inducing different macrophage activation states is discussed, as well as the role of hypoxia in the recruitment and activation of pro-angiogenic macrophages. In addition, the latest findings on the oncometabolites lactate and succinate in the activation of angiogenesis supporting macrophages are reviewed. Finally, various oncometabolite-targeting therapeutic strategies are proposed that could improve the response to anti-angiogenic therapies. SIGNIFICANCE STATEMENT: Tumor-associated macrophages (TAMs) are known promotors of tumor neovascularization, and significantly contribute to the emergence of resistance to anti-angiogenic therapies. Recent evidence suggests that the angiogenesis promoting phenotype of TAMs can be activated by hypoxic tumor cell-derived oncometabolites, including lactate and succinate. Here, the latest findings into the lactate- and succinate-mediated mechanistic activation of pro-angiogenic TAMs are reviewed, and therapeutic strategies that interfere with this mechanism and may delay or even prevent acquired resistance to anti-angiogenic agents are discussed.

A System Biology-Based Approach for Designing Combination Therapy in Cancer Precision Medicine

In this paper, we have used an agent-based stochastic tumor growth model and presented a mathematical and theoretical perspective to cancer therapy. This perspective can be used to theoretical study of precision medicine and combination therapy in individuals. We have conducted a series of in silico combination therapy experiments. Based on cancer drugs and new findings of cancer biology, we hypothesize relationships between model parameters which in some cases represent individual genome characteristics and cancer drugs, i.e., in our approach, therapy players are delegated by biologically reasonable parameters. In silico experiments showed that combined therapies are more effective when players affect tumor via different mechanisms and have different physical dimensions. This research presents for the first time an algorithm as a theoretical viewpoint for the prediction of effectiveness and classification of therapy sets.

Investigating the reactivity of neutral water-soluble Ru(ii)-PTA carbonyls towards the model imine ligands pyridine and 2,2'-bipyridine

As a continuation of our strategy for preparing new Ru(ii) precursors to be exploited as building blocks in the construction of metal-mediated supramolecular assemblies with improved solubility in water, here we describe the reactivity of selected neutral Ru(ii)-PTA carbonyls (PTA = 1,3,5-triaza-7-phosphaadamantane) towards the model imine ligands pyridine (py) and 2,2'-bipyridine (bpy) and the preparation and characterization of several neutral and cationic water-soluble derivatives: trans,trans,trans-[RuCl2(CO)(py)(PTA)2] (7), cis,cis,trans-[RuCl2(CO)2(py)(PTA)] (9), cis,trans-[Ru(bpy)Cl(CO)(PTA)2]Cl (10), mer-[Ru(bpy)(CO)(PTA)3](Cl)2 (12), cis,trans-[Ru(bpy)(CO)2Cl(PTA)]Cl (13), cis,trans-[Ru(bpy)(CO)2(PTA)2](NO3)2 (14NO3). In addition, we found that light-induced isomerization in some bpy compounds could be induced. The following species, either side-products isolated in low yield or compounds obtained exclusively in solution, were also unambiguously identified: cis,cis,trans-[RuCl2(CO)(py)(PTA)2] (8), trans-[RuCl2(bpy)(CO)(PTA)] (11), cis,cis-[Ru(bpy)Cl(CO)(PTA)2]Cl (15) and cis,cis-[Ru(bpy)(CO)2Cl(PTA)]Cl (16). The X-ray structures of 7, 11·H2O, and 12·7H2O are also reported. All compounds are new and - with few exceptions - show a good solubility in water.

Synergisms of genome and metabolism stabilizing antitumor therapy (GMSAT) in human breast and colon cancer cell lines: a novel approach to screen for synergism

BACKGROUND

Despite an improvement of prognosis in breast and colon cancer, the outcome of the metastatic disease is still severe. Microevolution of cancer cells often leads to drug resistance and tumor-recurrence. To target the driving forces of the tumor microevolution, we focused on synergistic drug combinations of selected compounds. The aim is to prevent the tumor from evolving in order to stabilize disease remission. To identify synergisms in a high number of compounds, we propose here a three-step concept that is cost efficient, independent of high-throughput machines and reliable in its predictions.

METHODS

We created dose response curves using MTT- and SRB-assays with 14 different compounds in MCF-7, HT-29 and MDA-MB-231 cells. In order to efficiently screen for synergies, we developed a screening tool in which 14 drugs were combined (91 combinations) in MCF-7 and HT-29 using EC25 or less. The most promising combinations were verified by the method of Chou and Talalay.

RESULTS

All 14 compounds exhibit antitumor effects on each of the three cell lines. The screening tool resulted in 19 potential synergisms detected in HT-29 (20.9%) and 27 in MCF-7 (29.7%). Seven of the top combinations were further verified over the whole dose response curve, and for five combinations a significant synergy could be confirmed. The combination Nutlin-3 (inhibition of MDM2) and PX-478 (inhibition of HIF-1α) could be confirmed for all three cell lines. The same accounts for the combination of Dichloroacetate (PDH activation) and NHI-2 (LDH-A inhibition). Our screening method proved to be an efficient tool that is reliable in its projections.

CONCLUSIONS

The presented three-step concept proved to be cost- and time-efficient with respect to the resulting data. The newly found combinations show promising results in MCF-7, HT-29 and MDA-MB231 cancer cells.

Project IDentif.AI: Harnessing Artificial Intelligence to Rapidly Optimize Combination Therapy Development for Infectious Disease Intervention

In 2019/2020, the emergence of coronavirus disease 2019 (COVID-19) resulted in rapid increases in infection rates as well as patient mortality. Treatment options addressing COVID-19 included drug repurposing, investigational therapies such as remdesivir, and vaccine development. Combination therapy based on drug repurposing is among the most widely pursued of these efforts. Multi-drug regimens are traditionally designed by selecting drugs based on their mechanism of action. This is followed by dose-finding to achieve drug synergy. This approach is widely-used for drug development and repurposing. Realizing synergistic combinations, however, is a substantially different outcome compared to globally optimizing combination therapy, which realizes the best possible treatment outcome by a set of candidate therapies and doses toward a disease indication. To address this challenge, the results of Project IDentif.AI (Identifying Infectious Disease Combination Therapy with Artificial Intelligence) are reported. An AI-based platform is used to interrogate a massive 12 drug/dose parameter space, rapidly identifying actionable combination therapies that optimally inhibit A549 lung cell infection by vesicular stomatitis virus within three days of project start. Importantly, a sevenfold difference in efficacy is observed between the top-ranked combination being optimally and sub-optimally dosed, demonstrating the critical importance of ideal drug and dose identification. This platform is disease indication and disease mechanism-agnostic, and potentially applicable to the systematic N-of-1 and population-wide design of highly efficacious and tolerable clinical regimens. This work also discusses key factors ranging from healthcare economics to global health policy that may serve to drive the broader deployment of this platform to address COVID-19 and future pandemics.

Machine learning approaches to drug response prediction: challenges and recent progress

Cancer is a leading cause of death worldwide. Identifying the best treatment using computational models to personalize drug response prediction holds great promise to improve patient's chances of successful recovery. Unfortunately, the computational task of predicting drug response is very challenging, partially due to the limitations of the available data and partially due to algorithmic shortcomings. The recent advances in deep learning may open a new chapter in the search for computational drug response prediction models and ultimately result in more accurate tools for therapy response. This review provides an overview of the computational challenges and advances in drug response prediction, and focuses on comparing the machine learning techniques to be of utmost practical use for clinicians and machine learning non-experts. The incorporation of new data modalities such as single-cell profiling, along with techniques that rapidly find effective drug combinations will likely be instrumental in improving cancer care.

Drug-Drug Interactions of Irinotecan, 5-Fluorouracil, Folinic Acid and Oxaliplatin and Its Activity in Colorectal Carcinoma Treatment

The combination of folinic acid, 5-fluorouracil, oxaliplatin and/or irinotecan (FOLFOXIRI) is the standard of care for metastatic colorectal cancer (CRC). This strategy inhibits tumor growth but provokes drug resistance and serious side effects. We aimed to improve FOLFOXIRI by optimization of the dosing and the sequence of drug administration. We employed an orthogonal array composite design and linear regression analysis to obtain cell line-specific drug combinations for four CRC cell lines (DLD1, SW620, HCT116, LS174T). Our results confirmed the synergy between folinic acid and 5-fluorouracil and additivity, or even antagonism, between the other drugs of the combination. The drug combination administered at clinical doses resulted in significantly higher antagonistic interactions compared to the low-dose optimized drug combination (ODC). We found that the concomitant administration of the optimized drug combination (ODC) was comparatively active to sequential administration. However, the administration of oxaliplatin or the active metabolite of irinotecan seemed to sensitize the cells to the combination of folinic acid and 5-fluorouracil. ODCs were similarly active in non-cancerous cells as compared to the clinically used doses, indicating a lack of reduction of side effects. Interestingly, ODCs were inactive in CRC cells chronically pretreated with FOLFOXIRI, suggesting the occurrence of resistance. We were unable to improve FOLFOXIRI in terms of efficacy or specificity. Improvement of CRC treatment should come from the optimization of targeted drugs and immunotherapy strategies.

Enabling Technologies for Personalized and Precision Medicine

Individualizing patient treatment is a core objective of the medical field. Reaching this objective has been elusive owing to the complex set of factors contributing to both disease and health; many factors, from genes to proteins, remain unknown in their role in human physiology. Accurately diagnosing, monitoring, and treating disorders requires advances in biomarker discovery, the subsequent development of accurate signatures that correspond with dynamic disease states, as well as therapeutic interventions that can be continuously optimized and modulated for dose and drug selection. This work highlights key breakthroughs in the development of enabling technologies that further the goal of personalized and precision medicine, and remaining challenges that, when addressed, may forge unprecedented capabilities in realizing truly individualized patient care.

Colorectal Cancer Growth Retardation through Induction of Apoptosis, Using an Optimized Synergistic Cocktail of Axitinib, Erlotinib, and Dasatinib

Patients with advanced colorectal cancer (CRC) still depend on chemotherapy regimens that are associated with significant limitations, including resistance and toxicity. The contribution of tyrosine kinase inhibitors (TKIs) to the prolongation of survival in these patients is limited, hampering clinical implementation. It is suggested that an optimal combination of appropriate TKIs can outperform treatment strategies that contain chemotherapy. We have previously identified a strongly synergistic drug combination (SDC), consisting of axitinib, erlotinib, and dasatinib that is active in renal cell carcinoma cells. In this study, we investigated the activity of this SDC in different CRC cell lines (SW620, HT29, and DLD-1) in more detail. SDC treatment significantly and synergistically decreased cell metabolic activity and induced apoptosis. The translation of the in-vitro-based results to in vivo conditions revealed significant CRC tumor growth inhibition, as evaluated in the chicken chorioallantoic membrane (CAM) model. Phosphoproteomics analysis of the tested cell lines revealed expression profiles that explained the observed activity. In conclusion, we demonstrate promising activity of an optimized mixture of axitinib, erlotinib, and dasatinib in CRC cells, and suggest further translational development of this drug mixture.

AI‐Enabled Parabolic Response Surface Approach Identifies Ultra Short‐Course Near‐Universal TB Drug Regimens

Tuberculosis (TB) is a major health problem that causes more deaths worldwide than any other single infectious disease. Current multidrug therapy for tuberculosis is exceedingly lengthy, leading to poor drug adherence, and consequently the emergence of drug resistance. Hence, much more rapid treatments are needed. Experimentally identifying the most synergistic drug combinations among available drugs is complicated by the astronomical number of possible drug-dose combinations. This problem is dealt with by the use of an artificial-intelligence-enabled parabolic response surface platform in conjunction with an in vitro Mycobacterium tuberculosis–infected macrophage cell culture assay amenable to high-throughput screening. This strategy allows rapid identification of the most effective drug-dose combinations by testing only a small fraction of the total drug-dose efficacy response surface. The same platform is then used to optimize the in vivo doses of each drug in the most potent regimens. Thus, regimens are identified that are dramatically more effective than the Standard Regimen in treating TB in a mouse model—a model broadly predictive of drug efficacy in humans. The most effective regimens reported herein shorten the duration of treatment required to achieve relapse-free cure by 80% and are suitable for treating both drug-sensitive and most drug-resistant cases of tuberculosis.

The role of glycolysis and mitochondrial respiration in the formation and functioning of endothelial tip cells during angiogenesis

During sprouting angiogenesis, an individual endothelial tip cell grows out from a pre-existing vascular network and guides following and proliferating stalk cells to form a new vessel. Metabolic pathways such as glycolysis and mitochondrial respiration as the major sources of adenosine 5'-triphosphate (ATP) for energy production are differentially activated in these types of endothelial cells (ECs) during angiogenesis. Therefore, we studied energy metabolism during angiogenesis in more detail in tip cell and non-tip cell human umbilical vein ECs. Small interfering RNA was used to inhibit transcription of glycolytic enzymes PFKFB3 or LDHA and mitochondrial enzyme PDHA1 to test whether inhibition of these specific pathways affects tip cell differentiation and sprouting angiogenesis in vitro and in vivo. We show that glycolysis is essential for tip cell differentiation, whereas both glycolysis and mitochondrial respiration occur during proliferation of non-tip cells and in sprouting angiogenesis in vitro and in vivo. Finally, we demonstrate that inhibition of mitochondrial respiration causes adaptation of EC metabolism by increasing glycolysis and vice versa. In conclusion, our studies show a complex but flexible role of the different metabolic pathways to produce ATP in the regulation of tip cell and non-tip cell differentiation and functioning during sprouting angiogenesis.

A nanodroplet cell processing platform facilitating drug synergy evaluations for anti-cancer treatments

Therapeutic drug synergism intervened in cancer treatments has been demonstrated to be more effective than using a single effector. However, it remains inherently challenging, with a limited cell count from tumor samples, to achieve potent personalized drug cocktails. To address the issue above, we herein present a nanodroplet cell processing platform. The platform incorporates an automatic nanodroplet dispenser with cell array ParaStamp chips, which were fabricated by a new wax stamping approach derived from laser direct writing. Such approach enables not only the on-demand de-wetting with hydrophobic wax films on substrates but also the mask-less fabrication of non-planar microstructures (i.e. no photolithography process). The ParaStamp chip was pre-occupied with anti-cancer drugs and their associate mixtures, enabling for the spatially addressable screening of optimal drug combinations simultaneously. Each droplet with a critical volume of 200 nl containing with 100 cells was utilized. Results revealed that the optimal combination reduces approximate 28-folds of conducted doses compared with single drugs. Tumor inhibition with the optimally selected drug combination was further confirmed by using PC-3 tumor-bearing mouse models. Together, the nanodroplet cell processing platform could therefore offer new opportunities to power the personalized cancer medicine at early-stage drug screening and discovery.

Noise-precision tradeoff in predicting combinations of mutations and drugs

Many biological problems involve the response to multiple perturbations. Examples include response to combinations of many drugs, and the effects of combinations of many mutations. Such problems have an exponentially large space of combinations, which makes it infeasible to cover the entire space experimentally. To overcome this problem, several formulae that predict the effect of drug combinations or fitness landscape values have been proposed. These formulae use the effects of single perturbations and pairs of perturbations to predict triplets and higher order combinations. Interestingly, different formulae perform best on different datasets. Here we use Pareto optimality theory to quantitatively explain why no formula is optimal for all datasets, due to an inherent bias-variance (noise-precision) tradeoff. We calculate the Pareto front of log-linear formulae and find that the optimal formula depends on properties of the dataset: the typical interaction strength and the experimental noise. This study provides an approach to choose a suitable prediction formula for a given dataset, in order to best overcome the combinatorial explosion problem.

Sometimes a combination of drugs works much better than each drug alone. Finding such drug cocktails is a pressing challenge in order to combat drug resistance and to improve drug effects. However, it is impossible to test all combinations of multiple drug experimentally. Therefore, researchers are looking for computational rather than experimental approaches to overcome this problem. One approach is to measure the effect of few drugs and plug it into a formula that predicts the effect of many drugs together. Existing prediction formulae typically perform best on the dataset that they were developed on, but less well on other datasets. Here we explain this observation and give a guide for the choice of an optimal prediction formula for a given dataset. The optimal formula depends on two main properties of the dataset: 1) The interaction strength between the drugs and 2) The experimental noise in the data. This study may help researchers discover effective combinations of multiple drugs and multiple perturbations in general.

Short-term 3D culture systems of various complexity for treatment optimization of colorectal carcinoma

Three-dimensional (3D) cultures have the potential to increase the predictive value of pre-clinical drug research and bridge the gap towards anticipating clinical outcome of proposed treatments. However, their implementation in more advanced drug-discovery programs is still in its infancy due to the lack of reproducibility and low time- and cost effectiveness. HCT116, SW620 and DLD1 cells, cell lines with distinct mutations, grade and origin, were co-cultured with fibroblasts and endothelial cells (EC) in 3D spheroids. Clinically relevant drugs, i.e. 5-fluorouracil (5−FU), regorafenib and erlotinib, were administered individually to in CRC cell cultures. In this study, we established a robust, low-cost and reproducible short-term 3D culture system addressing the various complexities of the colorectal carcinoma (CRC) microenvironment. We observed a dose-dependent increase of erlotinib sensitivity in 3D (co-)cultures compared to 2D cultures. Furthermore, we compared the drug combination efficacy and drug-drug interactions administered in 2D, 3D and 3D co-cultures. We observed that synergistic/additive drug-drug interactions for drug combinations administered at low doses shifted towards additive and antagonistic when applied at higher doses in metastatic CRC cells. The addition of fibroblasts at various ratios and EC increased the resistance to some drug combinations in SW620 and DLD1 cells, but not in HCT116. Retreatment of SW620 3D co-cultures with a low-dose 3-drug combination was as active (88% inhibition, relative to control) as 5-FU treatment at high dose (100 μM). Moreover, 3D and 3D co-cultures responded variably to the drug combination treatments, and also signalling pathways were differently regulated, probably due to the influence of fibroblasts and ECs on cancer cells. The short-term 3D co-culture system developed here is a powerful platform for screening (combination) therapies. Understanding of signalling in 3D co-cultures versus 3D cultures and the responses in the 3D models upon drug treatment might be beneficial for designing anti-cancer therapies.

Drug repurposing in oncology: Compounds, pathways, phenotypes and computational approaches for colorectal cancer

The strategy of using existing drugs originally developed for one disease to treat other indications has found success across medical fields. Such drug repurposing promises faster access of drugs to patients while reducing costs in the long and difficult process of drug development. However, the number of existing drugs and diseases, together with the heterogeneity of patients and diseases, notably including cancers, can make repurposing time consuming and inefficient. The key question we address is how to efficiently repurpose an existing drug to treat a given indication. As drug efficacy remains the main bottleneck for overall success, we discuss the need for machine-learning computational methods in combination with specific phenotypic studies along with mechanistic studies, chemical genetics and omics assays to successfully predict disease-drug pairs. Such a pipeline could be particularly important to cancer patients who face heterogeneous, recurrent and metastatic disease and need fast and personalized treatments. Here we focus on drug repurposing for colorectal cancer and describe selected therapeutics already repositioned for its prevention and/or treatment as well as potential candidates. We consider this review as a selective compilation of approaches and methodologies, and argue how, taken together, they could bring drug repurposing to the next level.