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.

Alisertib Synergistically Strengthens the Anti-Leukemia Activity of Venetoclax in TCF3-Hlf B-ALL

Background: Despite maximally-intensive chemotherapy and stem cell transplantation, survival of patients with the very rare t(17;19)/TCF3-HLF B-acute lymphoblastic leukemia (B-ALL) subtype remains effectively 0%. Prior studies have demonstrated association of the oncogenic TCF3-HLF fusion protein with multi-drug resistance via increased expression of ABC and P-glycoprotein drug efflux transporters, as well as via upregulation of pro-survival Ras and BCL-2 pathways. Preclinical studies and small clinical case series of targeted inhibitor addition to chemotherapy or antibody-based and cellular immunotherapies have aimed to improve outcomes for children with TCF3-HLF ALL. Unfortunately, targeting of these activated pathways with the BCL-2 inhibitor venetoclax or other small molecule inhibitors (SMIs) has been minimally or only transiently effective, suggesting more complex mechanism(s) of chemoresistance. In recent years, many patients with relapsed TCF3-HLF ALL have enrolled on clinical trials of CD19- or CD22-targeted immunotherapies. However, TCF3-HLF ALL frequently harbours deactivating mutations in PAX5, a major B-cell regulator and indispensable CD19 transcription factor, placing immunotherapy-treated patients at higher risk of CD19 antigen-loss relapse. New therapies remain needed to prevent relapse and attempt cure. Methods: We designed an unbiased kinome-wide CRISPR/Cas9 library to identify essential drivers in TCF3-HLF leukemogenesis. We screened the human TCF3-HLF ALL cell line HAL-01 and our TCF3-HLF ALL patient-derived xenograft (PDX) model ALL1807 (Hurtz JCI 2020, Schultz Genome Biol 2021), then validated identified targets using 49 SMIs targeting receptor tyrosine kinases (RTK), MEK signaling, and cell cycle pathways. We selected promising candidate inhibitor pairings with non-overlapping mechanisms of action and assessed for in vitro drug synergy via SynergyFinder analyses. Finally, we assessed the in vivo activity of targeted inhibitors in ALL1807 and two newly established TCF3-HLF ALL PDX models (CPCT-0002, CPCT-0003) created from primary pediatric specimens obtained via the LEAP Consortium (Pikman Cancer Disc 2021). Results: RNA-sequencing of HAL-01 and ALL1807 cells followed by functional protein association (STRING) analysis confirmed a network of significantly upregulated (>3-fold) plasma membrane and cytoplasm components of RTK pathways as well as BCL-2. The intersection of the results of the SMI drug library screen with the top 1% targets identified in CRIPSR/Cas9 screen determined p120-RasGAP and Aurora kinase A (AURKA) as therapeutic targets in TCF3-HLF ALL. In vitro treatment of HAL-01 or ALL1807 cells with the RasGAP inhibitor, pluripotin, or the AURKA inhibitor, alisertib, across a range of concentrations demonstrated robust anti-ALL activity. AURKA and RasGAP co-immunoprecipitated and this protein complex was disrupted with alisertib or pluripotin treatment. The AURKB inhibitor barisertib had minimal activity against TCF3-HLF ALL cells, confirming preferential dependency of these cells upon AURKA. Treatment of TCF3-HLF ALL cells with the BCL-2i venetoclax did not disrupt the AURKA/RasGAP complex, suggesting its different mechanism of action and potential for combinatorial drug therapy. Next, we found that alisertib and venetoclax synergistically killed TCF3-HLF ALL cells. Finally, we observed superior inhibition of in vivo leukemia with dual AURKA and BCL-2 inhibitor treatment of three TCF3-HLF ALL PDX models compared to single-agent alisertib or venetoclax (Figure 1). Conclusions: We identified AURKA as a critical new driver in TCF3-HLF ALL via orthogonal genetic and functional assays and confirmed prior observations of BCL-2 dependency in our models. We validated these key targets via in vitro and in vivo pharmacologic inhibition studies with drug synergy detected with combined alisertib and venetoclax in human TCF3-HLF ALL cell lines and PDX models. We posit that dual AURKA and BCL-2 inhibition is a clinically-pragmatic and potentially effective therapeutic strategy for patients with this rare, but highly fatal, leukemia subtype that merits formal clinical investigation. Figure 1 Figure 1. Disclosures Carroll: Incyte Pharmaceuticals: Research Funding; Janssen Pharmaceutical: Consultancy. Stegmaier: Auron Therapeutics, Kronos Bio, AstraZeneca, Novartis Institute of Biomedical Research: Consultancy, Research Funding. Tasian: Incyte Corporation: Research Funding; Gilead Sciences: Research Funding; Kura Oncology: Consultancy; Aleta Biotherapeutics: Consultancy.

Prediction of synergistic drug combinations using PCA-initialized deep learning

Background Cancer is one of the main causes of death worldwide. Combination drug therapy has been a mainstay of cancer treatment for decades and has been shown to reduce host toxicity and prevent the development of acquired drug resistance. However, the immense number of possible drug combinations and large synergistic space makes it infeasible to screen all effective drug pairs experimentally. Therefore, it is crucial to develop computational approaches to predict drug synergy and guide experimental design for the discovery of rational combinations for therapy. Results We present a new deep learning approach to predict synergistic drug combinations by integrating gene expression profiles from cell lines and chemical structure data. Specifically, we use principal component analysis (PCA) to reduce the dimensionality of the chemical descriptor data and gene expression data. We then propagate the low-dimensional data through a neural network to predict drug synergy values. We apply our method to O’Neil’s high-throughput drug combination screening data as well as a dataset from the AstraZeneca-Sanger Drug Combination Prediction DREAM Challenge. We compare the neural network approach with and without dimension reduction. Additionally, we demonstrate the effectiveness of our deep learning approach and compare its performance with three state-of-the-art machine learning methods: Random Forests, XGBoost, and elastic net, with and without PCA-based dimensionality reduction. Conclusions Our developed approach outperforms other machine learning methods, and the use of dimension reduction dramatically decreases the computation time without sacrificing accuracy.

Uncovering expression signatures of synergistic drug response using an ensemble of explainable AI models

Complex machine learning models are poised to revolutionize the treatment of diseases like acute myeloid leukemia (AML) by helping physicians choose optimal combinations of anti-cancer drugs based on molecular features. While accurate predictions are important, it is equally important to be able to learn about the underlying molecular basis of anti-cancer drug synergy. Explainable AI (XAI) offers a promising new route for data-driven cancer pharmacology, combining highly accurate models with interpretable insights into model decisions. Due to the highly correlated, high-dimensional nature of cancer transcriptomic data, however, we find that existing XAI approaches are suboptimal when applied naively to large transcriptomic datasets. We show how a novel approach based on model ensembling helps to increase the quality of explanations. We then use our method to demonstrate that a hematopoietic differentiation signature underlies synergy for a variety of anti-AML drug combinations.

Deep learning identifies synergistic drug combinations for treating COVID-19

Effective treatments for COVID-19 are urgently needed. However, discovering single-agent therapies with activity against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has been challenging. Combination therapies play an important role in antiviral therapies, due to their improved efficacy and reduced toxicity. Recent approaches have applied deep learning to identify synergistic drug combinations for diseases with vast preexisting datasets, but these are not applicable to new diseases with limited combination data, such as COVID-19. Given that drug synergy often occurs through inhibition of discrete biological targets, here we propose a neural network architecture that jointly learns drug−target interaction and drug−drug synergy. The model consists of two parts: a drug−target interaction module and a target−disease association module. This design enables the model to utilize drug−target interaction data and single-agent antiviral activity data, in addition to available drug−drug combination datasets, which may be small in nature. By incorporating additional biological information, our model performs significantly better in synergy prediction accuracy than previous methods with limited drug combination training data. We empirically validated our model predictions and discovered two drug combinations, remdesivir and reserpine as well as remdesivir and IQ-1S, which display strong antiviral SARS-CoV-2 synergy in vitro. Our approach, which was applied here to address the urgent threat of COVID-19, can be readily extended to other diseases for which a dearth of chemical−chemical combination data exists.

MuSyC is a consensus framework that unifies multi-drug synergy metrics for combinatorial drug discovery

Drug combination discovery depends on reliable synergy metrics but no consensus exists on the correct synergy criterion to characterize combined interactions. The fragmented state of the field confounds analysis, impedes reproducibility, and delays clinical translation of potential combination treatments. Here we present a mass-action based formalism to quantify synergy. With this formalism, we clarify the relationship between the dominant drug synergy principles, and present a mapping of commonly used frameworks onto a unified synergy landscape. From this, we show how biases emerge due to intrinsic assumptions which hinder their broad applicability and impact the interpretation of synergy in discovery efforts. Specifically, we describe how traditional metrics mask consequential synergistic interactions, and contain biases dependent on the Hill-slope and maximal effect of single-drugs. We show how these biases systematically impact synergy classification in large combination screens, potentially misleading discovery efforts. Thus the proposed formalism can provide a consistent, unbiased interpretation of drug synergy, and accelerate the translatability of synergy studies.

The IDentif.AI 2.0 Pandemic Readiness Platform: Rapid Prioritization of Optimized COVID-19 Combination Therapy Regimens

Objectives: We aimed to harness IDentif.AI 2.0, a clinically actionable AI platform to rapidly pinpoint and prioritize optimal combination therapy regimens against COVID-19. Methods: A pool of starting candidate therapies was developed in collaboration with a community of infectious disease clinicians and included EIDD-1931 (metabolite of EIDD-2801), baricitinib, ebselen, selinexor, masitinib, nafamostat mesylate, telaprevir (VX-950), SN-38 (metabolite of irinotecan), imatinib mesylate, remdesivir, lopinavir, and ritonavir. Following the initial drug pool assessment, a focused, 6-drug pool was interrogated at 3 dosing levels per drug representing nearly 10,000 possible combination regimens. IDentif.AI 2.0 paired prospective, experimental validation of multi-drug efficacy on a SARS-CoV-2 live virus (propagated, original strain and B.1.351 variant) and Vero E6 assay with a quadratic optimization workflow. Results: Within 3 weeks, IDentif.AI 2.0 realized a list of combination regimens, ranked by efficacy, for clinical go/no-go regimen recommendations. IDentif.AI 2.0 revealed EIDD-1931 to be a strong candidate upon which multiple drug combinations can be derived. Conclusions: IDentif.AI 2.0 rapidly revealed promising drug combinations for a clinical translation. It pinpointed dose-dependent drug synergy behavior to play a role in trial design and realizing positive treatment outcomes. IDentif.AI 2.0 represents an actionable path towards rapidly optimizing combination therapy following pandemic emergence.

Systematic Prediction of Antifungal Drug Synergy by Chemogenomic Screening in Saccharomyces cerevisiae

Since the earliest days of using natural remedies, combining therapies for disease treatment has been standard practice. Combination treatments exhibit synergistic effects, broadly defined as a greater-than-additive effect of two or more therapeutic agents. Clinicians often use their experience and expertise to tailor such combinations to maximize the therapeutic effect. Although understanding and predicting biophysical underpinnings of synergy have benefitted from high-throughput screening and computational studies, one challenge is how to best design and analyze the results of synergy studies, especially because the number of possible combinations to test quickly becomes unmanageable. Nevertheless, the benefits of such studies are clear—by combining multiple drugs in the treatment of infectious disease and cancer, for instance, one can lessen host toxicity and simultaneously reduce the likelihood of resistance to treatment. This study introduces a new approach to characterize drug synergy, in which we extend the widely validated chemogenomic HIP–HOP assay to drug combinations; this assay involves parallel screening of comprehensive collections of barcoded deletion mutants. We identify a class of “combination-specific sensitive strains” that introduces mechanisms for the synergies we observe and further suggest focused follow-up studies.